def build_composite_clinical_item(components, name, description,
category_id):
"""
Simple wrapper around medinfo/cpoe/DataManager.py
"""
component_str = ','.join([str(id) for id in components])
log.debug('(%s, %s, %s) = (%s)' % (name, description, category_id, \
component_str))
composite_arg = '%s|%s|%s|%s' % (component_str, name, description, \
category_id)
dm = DataManager()
dm.main([
'medinfo/cpoe/DataManager.py', '--compositeRelated', composite_arg
])
def action_updateCounts(self):
# Update the summary counts to facilitate future rapid queries
dataManager = DataManager()
dataManager.updateClinicalItemCounts()
class TestOutcomePredictionAnalysis(DBTestCase):
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
log.info("Populate the database with test data")
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
RowItemModel( [-7, "Admit Dx"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = [
"clinical_item_id", "clinical_item_category_id", "analysis_status",
"name"
]
dataModels = \
[
RowItemModel( [-1, -1, 1, "CBC"], headers ),
RowItemModel( [-2, -1, 1, "BMP"], headers ),
RowItemModel( [-3, -1, 1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, 1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, 1, "CXR"], headers ),
RowItemModel( [-6, -2, 1, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, 1, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, 1, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, 1, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, 1, "Carvedilol"], headers ),
RowItemModel( [-11, -3, 1, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, 1, "Warfarin"], headers ),
RowItemModel( [-13, -3, 1, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, 1, "Admit"], headers ),
RowItemModel( [-15, -4, 1, "Discharge"], headers ),
RowItemModel( [-16, -4, 1, "Readmit"], headers ),
RowItemModel( [-22, -5, 1, "Diagnosis 2"], headers ),
RowItemModel( [-23, -5, 1, "Diagnosis 3"], headers ),
RowItemModel( [-24, -5, 1, "Diagnosis 4"], headers ),
RowItemModel( [-30, -6, 1, "Troponin (High)"], headers ),
RowItemModel( [-31, -6, 1, "BNP (High)"], headers ),
RowItemModel( [-32, -6, 1, "Creatinine (High)"], headers ),
RowItemModel( [-33, -6, 1, "ESR (High)"], headers ),
RowItemModel( [-21, -7, 0, "Diagnosis 1"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-52, -11111, -23, datetime(1999, 9, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-51, -11111, -21, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 1), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-60, -11111, -32, datetime(2000, 1, 1, 4), datetime(2010, 1, 1, 0)], headers ), # Within query time
RowItemModel( [-61, -11111, -30, datetime(2000, 1, 4, 0), datetime(2010, 1, 1, 0)], headers ), # Within 1 week
RowItemModel( [-62, -11111, -31, datetime(2000, 1,10, 0), datetime(2010, 1, 1, 0)], headers ), # Past 1 week
RowItemModel( [-55, -22222, -21, datetime(2000, 1, 8, 0), datetime(2010, 1, 1, 0)], headers ), # Admit Dx
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 8, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -14, datetime(2000, 1, 8, 1), datetime(2010, 1, 1, 0)], headers ), # Admit
RowItemModel( [-14, -22222, -7, datetime(2000, 1, 8, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-15, -22222, -8, datetime(2000, 1, 8, 3), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-16, -22222, -15, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ), # Discharge
RowItemModel( [-56, -22222, -21, datetime(2000, 1,13, 0), datetime(2010, 1, 1, 0)], headers ), # Admit Dx
RowItemModel( [-17, -22222, -9, datetime(2000, 1,13, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-19, -22222, -14, datetime(2000, 1,13, 1), datetime(2010, 1, 1, 0)], headers ), # Admit (Readmit)
RowItemModel( [-20, -22222, -10, datetime(2000, 1,13, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-21, -22222, -11, datetime(2000, 1,13, 3), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-22, -22222, -15, datetime(2000, 1,18, 0), datetime(2010, 1, 1, 0)], headers ), # Discharge
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = \
[ "clinical_item_id","subsequent_item_id",
"count_0","count_3600","count_86400","count_604800","count_any",
"time_diff_sum", "time_diff_sum_squares",
]
dataModels = \
[
RowItemModel( [ -1, -1, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -2, -2, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -3, -3, 95, 95, 97, 97, 97, 0.0, 0.0], headers ),
RowItemModel( [ -4, -4, 40, 40, 40, 40, 40, 0.0, 0.0], headers ),
RowItemModel( [ -5, -5, 40, 40, 50, 50, 50, 0.0, 0.0], headers ),
RowItemModel( [ -6, -6, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -7, -7, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -8, -8, 35, 35, 35, 50, 80, 0.0, 0.0], headers ),
RowItemModel( [-10,-10, 45, 45, 55, 60, 90, 0.0, 0.0], headers ),
RowItemModel( [-12,-12, 75, 75, 75, 80, 90, 0.0, 0.0], headers ),
RowItemModel( [-14,-14, 100, 100, 100, 100, 100, 0.0, 0.0], headers ),
RowItemModel( [-15,-15, 100, 100, 100, 100, 100, 0.0, 0.0], headers ),
RowItemModel( [-16,-16, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [-30,-30, 3, 3, 3, 3, 3, 0.0, 0.0], headers ),
RowItemModel( [-31,-31, 4, 4, 4, 4, 4, 0.0, 0.0], headers ),
RowItemModel( [-32,-32, 4, 4, 4, 4, 4, 0.0, 0.0], headers ),
RowItemModel( [-33,-33, 5, 5, 5, 5, 5, 0.0, 0.0], headers ),
RowItemModel( [ -2, -4, 0, 2, 3, 3, 3, 200.0, 50000.0], headers ),
RowItemModel( [ -2, -6, 2, 2, 5, 5, 5, 300.0, 11990.0], headers ),
RowItemModel( [ -3, -1, 20, 23, 23, 23, 23, 400.0, 344990.0], headers ),
RowItemModel( [ -4, -5, 3, 3, 13, 43, 43, 340.0, 343110.0], headers ),
RowItemModel( [ -4, -6, 23, 33, 33, 33, 63, 420.0, 245220.0], headers ),
RowItemModel( [ -4, -7, 27, 33, 33, 33, 63, 40.0, 5420.0], headers ),
RowItemModel( [ -4,-10, 25, 35, 40, 45, 63, 47.0, 5420.0], headers ),
RowItemModel( [ -5, -4, 0, 0, 20, 20, 20, 540.0, 54250.0], headers ),
RowItemModel( [ -6,-16, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [ -8,-16, 5, 5, 5, 5, 5, 0.0, 0.0], headers ),
RowItemModel( [-10,-16, 8, 8, 8, 8, 8, 0.0, 0.0], headers ),
RowItemModel( [-10,-30, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [-10,-31, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [-12,-30, 20, 20, 20, 20, 20, 0.0, 0.0], headers ),
RowItemModel( [-12,-31, 20, 20, 20, 20, 20, 0.0, 0.0], headers ),
RowItemModel( [-10,-32, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [-10,-33, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
# Indicate that cache data needs to be updated
self.dataManager = DataManager()
self.dataManager.clearCacheData("analyzedPatientCount")
self.dataManager.clearCacheData("clinicalItemCountsUpdated")
# Instance to test on
self.analyzer = OutcomePredictionAnalysis()
self.preparer = PreparePatientItems()
def tearDown(self):
"""Restore state from any setUp or test steps"""
log.info("Purge test records from the database")
DBUtil.execute(
"delete from clinical_item_association where clinical_item_id < 0")
DBUtil.execute("delete from patient_item where patient_item_id < 0")
DBUtil.execute("delete from clinical_item where clinical_item_id < 0")
DBUtil.execute(
"delete from clinical_item_category where clinical_item_category_id in (%s)"
% str.join(",", self.clinicalItemCategoryIdStrList))
DBTestCase.tearDown(self)
def test_recommenderAnalysis(self):
# Run the recommender against the mock test data above and verify expected stats afterwards.
analysisQuery = AnalysisQuery()
analysisQuery.patientIds = set([-11111])
analysisQuery.baseCategoryId = -7
analysisQuery.queryTimeSpan = timedelta(0, 86400)
#analysisQuery.recommender = BaselineFrequencyRecommender();
analysisQuery.recommender = ItemAssociationRecommender()
analysisQuery.baseRecQuery = RecommenderQuery()
analysisQuery.baseRecQuery.targetItemIds = set([-33, -32, -31, -30])
analysisQuery.baseRecQuery.maxRecommendedId = 0
# Restrict to test data
# Initial run without time limits on outcome measure
colNames = [
"patient_id", "outcome.-33", "score.-33", "outcome.-32",
"score.-32", "outcome.-31", "score.-31", "outcome.-30", "score.-30"
]
expectedResults = [
RowItemModel([-11111, +0, 0.222, +2, 0.611, +1, 0.222, +1, 0.222],
colNames)
]
analysisResults = self.analyzer(analysisQuery)
self.assertEqualStatResults(expectedResults, analysisResults, colNames)
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"OutcomePredictionAnalysis.py", "-c", "-7", "-Q", "86400", "-o",
"-33,-32,-31,-30", "-m", "0", "-R", "ItemAssociationRecommender",
'0,-11111', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualStatResultsTextOutput(expectedResults, textOutput,
colNames)
# Redo through prepared file intermediary
sys.stdout = StringIO()
argv = [
"PreparePatientItems.py", "-c", "-7", "-Q", "86400", "-V", "86400",
"-o", "-33,-32,-31,-30", '0,-11111', "-"
]
self.preparer.main(argv)
preparedDataFile = StringIO(sys.stdout.getvalue())
sys.stdin = preparedDataFile
# Read prepared data file from redirected stdin
sys.stdout = StringIO()
argv = [
"OutcomePredictionAnalysis.py", "-P", "-m", "0", "-R",
"ItemAssociationRecommender", '-', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualStatResultsTextOutput(expectedResults, textOutput,
colNames)
# Now try with time limitation on outcome measure
analysisQuery.baseRecQuery.timeDeltaMax = timedelta(0, 604800)
# 1 week
colNames = [
"patient_id", "outcome.-33", "score.-33", "outcome.-32",
"score.-32", "outcome.-31", "score.-31", "outcome.-30", "score.-30"
]
expectedResults = [
RowItemModel([-11111, +0, 0.222, +2, 0.611, +0, 0.222, +1, 0.222],
colNames)
]
analysisResults = self.analyzer(analysisQuery)
self.assertEqualStatResults(expectedResults, analysisResults, colNames)
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"OutcomePredictionAnalysis.py", "-c", "-7", "-Q", "86400", "-t",
"604800", "-o", "-33,-32,-31,-30", "-m", "0", "-R",
"ItemAssociationRecommender", '0,-11111', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualStatResultsTextOutput(expectedResults, textOutput,
colNames)
# Redo through prepared file intermediary
sys.stdout = StringIO()
argv = [
"PreparePatientItems.py", "-c", "-7", "-Q", "86400", "-V", "86400",
"-t", "604800", "-o", "-33,-32,-31,-30", '0,-11111', "-"
]
self.preparer.main(argv)
preparedDataFile = StringIO(sys.stdout.getvalue())
sys.stdin = preparedDataFile
# Read prepared data file from redirected stdin
sys.stdout = StringIO()
argv = [
"OutcomePredictionAnalysis.py", "-P", "-m", "0", "-R",
"ItemAssociationRecommender", "-t", "604800", '-', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualStatResultsTextOutput(expectedResults, textOutput,
colNames)
# Again, but with much stricter time limit (negative test case)
analysisQuery.baseRecQuery.timeDeltaMax = timedelta(0, 172800)
# 2 day
colNames = [
"patient_id", "outcome.-33", "score.-33", "outcome.-32",
"score.-32", "outcome.-31", "score.-31", "outcome.-30", "score.-30"
]
expectedResults = [
RowItemModel([-11111, 0, 0.0109, 2, 0.0600, 0, 0.0109, 0, 0.0109],
colNames)
]
analysisResults = self.analyzer(analysisQuery)
self.assertEqualStatResults(expectedResults, analysisResults, colNames)
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"OutcomePredictionAnalysis.py", "-c", "-7", "-Q", "86400", "-t",
"172800", "-o", "-33,-32,-31,-30", "-m", "0", "-R",
"ItemAssociationRecommender", '0,-11111', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualStatResultsTextOutput(expectedResults, textOutput,
colNames)
# Redo through prepared file intermediary
sys.stdout = StringIO()
argv = [
"PreparePatientItems.py", "-c", "-7", "-Q", "86400", "-V", "86400",
"-t", "172800", "-o", "-33,-32,-31,-30", '0,-11111', "-"
]
self.preparer.main(argv)
preparedDataFile = StringIO(sys.stdout.getvalue())
sys.stdin = preparedDataFile
# Read prepared data file from redirected stdin
sys.stdout = StringIO()
argv = [
"OutcomePredictionAnalysis.py", "-P", "-m", "0", "-R",
"ItemAssociationRecommender", "-t", "172800", '-', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualStatResultsTextOutput(expectedResults, textOutput,
colNames)
def test_tripleSequence_virtualItem(self):
# Test outcome assessment when the target is a virtual item based on the presence of a triple (instead of double) sequence of items
# Run the recommender against the mock test data above and verify expected stats afterwards.
analysisQuery = AnalysisQuery()
analysisQuery.patientIds = set([-22222])
analysisQuery.baseCategoryId = -7
analysisQuery.queryTimeSpan = timedelta(0, 86400)
analysisQuery.sequenceItemIdsByVirtualItemId[-16] = (-15, -14)
#analysisQuery.recommender = BaselineFrequencyRecommender();
analysisQuery.recommender = ItemAssociationRecommender()
analysisQuery.baseRecQuery = RecommenderQuery()
analysisQuery.baseRecQuery.targetItemIds = set([-16])
analysisQuery.baseRecQuery.maxRecommendedId = 0
# Restrict to test data
# Initial run without time limits on outcome measure
colNames = ["patient_id", "outcome.-16", "score.-16"]
expectedResults = [RowItemModel([-22222, +1, 0.14286], colNames)]
analysisResults = self.analyzer(analysisQuery)
self.assertEqualStatResults(expectedResults, analysisResults, colNames)
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"OutcomePredictionAnalysis.py", "-c", "-7", "-Q", "86400", "-o",
"-16=-15:-14", "-m", "0", "-R", "ItemAssociationRecommender",
'0,-22222', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualStatResultsTextOutput(expectedResults, textOutput,
colNames)
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
log.info("Populate the database with test data")
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
RowItemModel( [-7, "Admit Dx"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = [
"clinical_item_id", "clinical_item_category_id", "analysis_status",
"name"
]
dataModels = \
[
RowItemModel( [-1, -1, 1, "CBC"], headers ),
RowItemModel( [-2, -1, 1, "BMP"], headers ),
RowItemModel( [-3, -1, 1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, 1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, 1, "CXR"], headers ),
RowItemModel( [-6, -2, 1, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, 1, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, 1, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, 1, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, 1, "Carvedilol"], headers ),
RowItemModel( [-11, -3, 1, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, 1, "Warfarin"], headers ),
RowItemModel( [-13, -3, 1, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, 1, "Admit"], headers ),
RowItemModel( [-15, -4, 1, "Discharge"], headers ),
RowItemModel( [-16, -4, 1, "Readmit"], headers ),
RowItemModel( [-22, -5, 1, "Diagnosis 2"], headers ),
RowItemModel( [-23, -5, 1, "Diagnosis 3"], headers ),
RowItemModel( [-24, -5, 1, "Diagnosis 4"], headers ),
RowItemModel( [-30, -6, 1, "Troponin (High)"], headers ),
RowItemModel( [-31, -6, 1, "BNP (High)"], headers ),
RowItemModel( [-32, -6, 1, "Creatinine (High)"], headers ),
RowItemModel( [-33, -6, 1, "ESR (High)"], headers ),
RowItemModel( [-21, -7, 0, "Diagnosis 1"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-52, -11111, -23, datetime(1999, 9, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-51, -11111, -21, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 1), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-60, -11111, -32, datetime(2000, 1, 1, 4), datetime(2010, 1, 1, 0)], headers ), # Within query time
RowItemModel( [-61, -11111, -30, datetime(2000, 1, 4, 0), datetime(2010, 1, 1, 0)], headers ), # Within 1 week
RowItemModel( [-62, -11111, -31, datetime(2000, 1,10, 0), datetime(2010, 1, 1, 0)], headers ), # Past 1 week
RowItemModel( [-55, -22222, -21, datetime(2000, 1, 8, 0), datetime(2010, 1, 1, 0)], headers ), # Admit Dx
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 8, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -14, datetime(2000, 1, 8, 1), datetime(2010, 1, 1, 0)], headers ), # Admit
RowItemModel( [-14, -22222, -7, datetime(2000, 1, 8, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-15, -22222, -8, datetime(2000, 1, 8, 3), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-16, -22222, -15, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ), # Discharge
RowItemModel( [-56, -22222, -21, datetime(2000, 1,13, 0), datetime(2010, 1, 1, 0)], headers ), # Admit Dx
RowItemModel( [-17, -22222, -9, datetime(2000, 1,13, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-19, -22222, -14, datetime(2000, 1,13, 1), datetime(2010, 1, 1, 0)], headers ), # Admit (Readmit)
RowItemModel( [-20, -22222, -10, datetime(2000, 1,13, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-21, -22222, -11, datetime(2000, 1,13, 3), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-22, -22222, -15, datetime(2000, 1,18, 0), datetime(2010, 1, 1, 0)], headers ), # Discharge
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = \
[ "clinical_item_id","subsequent_item_id",
"count_0","count_3600","count_86400","count_604800","count_any",
"time_diff_sum", "time_diff_sum_squares",
]
dataModels = \
[
RowItemModel( [ -1, -1, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -2, -2, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -3, -3, 95, 95, 97, 97, 97, 0.0, 0.0], headers ),
RowItemModel( [ -4, -4, 40, 40, 40, 40, 40, 0.0, 0.0], headers ),
RowItemModel( [ -5, -5, 40, 40, 50, 50, 50, 0.0, 0.0], headers ),
RowItemModel( [ -6, -6, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -7, -7, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -8, -8, 35, 35, 35, 50, 80, 0.0, 0.0], headers ),
RowItemModel( [-10,-10, 45, 45, 55, 60, 90, 0.0, 0.0], headers ),
RowItemModel( [-12,-12, 75, 75, 75, 80, 90, 0.0, 0.0], headers ),
RowItemModel( [-14,-14, 100, 100, 100, 100, 100, 0.0, 0.0], headers ),
RowItemModel( [-15,-15, 100, 100, 100, 100, 100, 0.0, 0.0], headers ),
RowItemModel( [-16,-16, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [-30,-30, 3, 3, 3, 3, 3, 0.0, 0.0], headers ),
RowItemModel( [-31,-31, 4, 4, 4, 4, 4, 0.0, 0.0], headers ),
RowItemModel( [-32,-32, 4, 4, 4, 4, 4, 0.0, 0.0], headers ),
RowItemModel( [-33,-33, 5, 5, 5, 5, 5, 0.0, 0.0], headers ),
RowItemModel( [ -2, -4, 0, 2, 3, 3, 3, 200.0, 50000.0], headers ),
RowItemModel( [ -2, -6, 2, 2, 5, 5, 5, 300.0, 11990.0], headers ),
RowItemModel( [ -3, -1, 20, 23, 23, 23, 23, 400.0, 344990.0], headers ),
RowItemModel( [ -4, -5, 3, 3, 13, 43, 43, 340.0, 343110.0], headers ),
RowItemModel( [ -4, -6, 23, 33, 33, 33, 63, 420.0, 245220.0], headers ),
RowItemModel( [ -4, -7, 27, 33, 33, 33, 63, 40.0, 5420.0], headers ),
RowItemModel( [ -4,-10, 25, 35, 40, 45, 63, 47.0, 5420.0], headers ),
RowItemModel( [ -5, -4, 0, 0, 20, 20, 20, 540.0, 54250.0], headers ),
RowItemModel( [ -6,-16, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [ -8,-16, 5, 5, 5, 5, 5, 0.0, 0.0], headers ),
RowItemModel( [-10,-16, 8, 8, 8, 8, 8, 0.0, 0.0], headers ),
RowItemModel( [-10,-30, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [-10,-31, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [-12,-30, 20, 20, 20, 20, 20, 0.0, 0.0], headers ),
RowItemModel( [-12,-31, 20, 20, 20, 20, 20, 0.0, 0.0], headers ),
RowItemModel( [-10,-32, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
RowItemModel( [-10,-33, 10, 10, 10, 10, 10, 0.0, 0.0], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
# Indicate that cache data needs to be updated
self.dataManager = DataManager()
self.dataManager.clearCacheData("analyzedPatientCount")
self.dataManager.clearCacheData("clinicalItemCountsUpdated")
# Instance to test on
self.analyzer = OutcomePredictionAnalysis()
self.preparer = PreparePatientItems()
class TestDataManager(DBTestCase):
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
log.info("Populate the database with test data")
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = [
"clinical_item_id", "clinical_item_category_id", "name",
"analysis_status"
]
dataModels = \
[
RowItemModel( [-1, -1, "CBC",1], headers ),
RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
RowItemModel( [-5, -2, "CXR",1], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
RowItemModel( [-10, -3, "Carvedilol",1], headers ),
RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
RowItemModel( [-12, -3, "Warfarin",1], headers ),
RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
RowItemModel( [-15, -4, "Strict I&O",1], headers ),
RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
self.clinicalItemQuery = \
"""
select
clinical_item_id, name, analysis_status, default_recommend
from
clinical_item
where
clinical_item_id < 0
order by
clinical_item_id desc
"""
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-4, -11111, -10, datetime(2000, 1, 2, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-10, -22222, -7, datetime(2000, 1, 5, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 9, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -11, datetime(2000, 1, 9, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-14, -33333, -6, datetime(2000, 2, 9, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-15, -33333, -2, datetime(2000, 2,11, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-16, -33333, -11, datetime(2000, 2,11, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-17, -33333, -11, datetime(2001, 1, 1, 0), datetime(2100, 1, 1, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
self.patientItemQuery = \
"""
select
patient_item_id, patient_id, clinical_item_id, item_date, analyze_date
from
patient_item
where
clinical_item_id < 0
order by
patient_id desc, item_date, patient_item_id desc
"""
headers = [ "clinical_item_id","subsequent_item_id",\
"count_0","count_3600","count_86400","count_604800","count_any",
"time_diff_sum","time_diff_sum_squares",
"patient_count_0","patient_count_3600","patient_count_86400","patient_count_604800","patient_count_any",
"patient_time_diff_sum","patient_time_diff_sum_squares",
"patient_count_0","encounter_count_0",
]
dataModels = \
[
RowItemModel( [-11,-11, 3, 3, 3, 3, 4, 999.0, 9999.0, 2, 2, 2, 2, 2, 999.0, 9999.0, 2,2], headers ),
RowItemModel( [-11, -7, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0,0], headers ),
RowItemModel( [-11, -6, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [-11, -2, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -7,-11, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0,0], headers ),
RowItemModel( [ -7, -7, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -7, -6, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0,0], headers ),
RowItemModel( [ -6,-11, 1, 1, 1, 2, 2, 172800.0, 29859840000.0, 1, 1, 1, 2, 2, 172800.0, 29859840000.0, 1,1], headers ),
RowItemModel( [ -6, -7, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0,0], headers ),
RowItemModel( [ -6, -6, 2, 2, 2, 2, 2, 0.0, 0.0, 2, 2, 2, 2, 2, 0.0, 0.0, 2,2], headers ),
RowItemModel( [ -6, -2, 0, 0, 0, 1, 1, 172800.0, 29859840000.0, 0, 0, 0, 1, 1, 172800.0, 29859840000.0, 0,0], headers ),
RowItemModel( [ -2,-11, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -2, -7, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -2, -6, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0,0], headers ),
RowItemModel( [ -2, -2, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
self.clinicalItemAssociationQuery = \
"""
select
clinical_item_id, subsequent_item_id,
count_0,count_3600,count_86400,count_604800,count_any,
time_diff_sum,time_diff_sum_squares,
patient_count_0,patient_count_3600,patient_count_86400,patient_count_604800,patient_count_any,
patient_time_diff_sum, patient_time_diff_sum_squares
from
clinical_item_association
where
clinical_item_id < 0
order by
clinical_item_id, subsequent_item_id
"""
self.analyzer = DataManager()
# Instance to test on
self.analyzer.maxClinicalItemId = 0
# Avoid testing on "real" data
def tearDown(self):
"""Restore state from any setUp or test steps"""
log.info("Purge test records from the database")
DBUtil.execute(
"delete from clinical_item_link where clinical_item_id < 0")
DBUtil.execute(
"delete from backup_link_patient_item where patient_item_id < 0")
DBUtil.execute(
"delete from clinical_item_association where clinical_item_id < 0")
DBUtil.execute("delete from patient_item where patient_id < 0")
DBUtil.execute("delete from clinical_item where clinical_item_id < 0")
DBUtil.execute(
"delete from clinical_item_category where clinical_item_category_id in (%s)"
% str.join(",", self.clinicalItemCategoryIdStrList))
DBTestCase.tearDown(self)
def test_deactivateAnalysis(self):
clinicalItemIds = set([-6, -11])
self.analyzer.deactivateAnalysis(clinicalItemIds)
expectedClinicalItemStatus = \
[
[-1, "CBC",1, 1],
[-2, "BMP",0, 1],
[-3, "Hepatic Panel",1, 1],
[-4, "Cardiac Enzymes",1, 1],
[-5, "CXR",1, 1],
[-6, "RUQ Ultrasound",0, 1],
[-7, "CT Abdomen/Pelvis",1, 1],
[-8, "CT PE Thorax",1, 1],
[-9, "Acetaminophen",1, 1],
[-10, "Carvedilol",1, 1],
[-11, "Enoxaparin",0, 1],
[-12, "Warfarin",1, 1],
[-13, "Ceftriaxone",1, 1],
[-14, "Foley Catheter",1, 1],
[-15, "Strict I&O",1, 1],
[-16, "Fall Precautions",1, 1],
]
clinicalItemStatus = DBUtil.execute(self.clinicalItemQuery)
self.assertEqualTable(expectedClinicalItemStatus, clinicalItemStatus)
expectedPatientItems = \
[ # Use placeholder "*" for analyze date, just verify that it exists and is consistent. Actual value is not important
[-1, -11111, -4, datetime(2000, 1, 1, 0), "*"],
[-2, -11111, -10, datetime(2000, 1, 1, 0), "*"],
[-3, -11111, -8, datetime(2000, 1, 1, 2), "*"],
[-4, -11111, -10, datetime(2000, 1, 2, 0), "*"],
[-5, -11111, -12, datetime(2000, 2, 1, 0), "*"],
[-10, -22222, -7, datetime(2000, 1, 5, 0), "*"],
[-12, -22222, -6, datetime(2000, 1, 9, 0), None],
[-13, -22222, -11, datetime(2000, 1, 9, 0), None],
[-14, -33333, -6, datetime(2000, 2, 9, 0), None],
[-15, -33333, -2, datetime(2000, 2,11, 0), "*"],
[-16, -33333, -11, datetime(2000, 2,11, 0), None],
[-17, -33333, -11, datetime(2001, 1, 1, 0), None],
]
patientItems = DBUtil.execute(self.patientItemQuery)
self.assertEqualPatientItems(expectedPatientItems, patientItems)
expectedAssociationStats = \
[
[ -7, -7, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0],
[ -2, -7, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0],
[ -2, -2, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0],
]
associationStats = DBUtil.execute(self.clinicalItemAssociationQuery)
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
def test_deactivateAnalysisByCount(self):
thresholdInstanceCount = 1
categoryIds = [-1, -2]
self.analyzer.deactivateAnalysisByCount(thresholdInstanceCount,
categoryIds)
expectedClinicalItemStatus = \
[
[-1, "CBC",0, 1],
[-2, "BMP",0, 1],
[-3, "Hepatic Panel",0, 1],
[-4, "Cardiac Enzymes",0, 1],
[-5, "CXR",0, 1],
[-6, "RUQ Ultrasound",1, 1],
[-7, "CT Abdomen/Pelvis",0, 1],
[-8, "CT PE Thorax",0, 1],
[-9, "Acetaminophen",1, 1], # Different category, so should be left alone
[-10, "Carvedilol",1, 1],
[-11, "Enoxaparin",1, 1],
[-12, "Warfarin",1, 1],
[-13, "Ceftriaxone",1, 1],
[-14, "Foley Catheter",1, 1],
[-15, "Strict I&O",1, 1],
[-16, "Fall Precautions",1, 1],
]
clinicalItemStatus = DBUtil.execute(self.clinicalItemQuery)
self.assertEqualTable(expectedClinicalItemStatus, clinicalItemStatus)
expectedPatientItems = \
[ # Use placeholder "*" for analyze date, just verify that it exists and is consistent. Actual value is not important
[-1, -11111, -4, datetime(2000, 1, 1, 0), None],
[-2, -11111, -10, datetime(2000, 1, 1, 0), "*"],
[-3, -11111, -8, datetime(2000, 1, 1, 2), None],
[-4, -11111, -10, datetime(2000, 1, 2, 0), "*"],
[-5, -11111, -12, datetime(2000, 2, 1, 0), "*"],
[-10, -22222, -7, datetime(2000, 1, 5, 0), None],
[-12, -22222, -6, datetime(2000, 1, 9, 0), "*"],
[-13, -22222, -11, datetime(2000, 1, 9, 0), "*"],
[-14, -33333, -6, datetime(2000, 2, 9, 0), "*"],
[-15, -33333, -2, datetime(2000, 2,11, 0), None],
[-16, -33333, -11, datetime(2000, 2,11, 0), "*"],
[-17, -33333, -11, datetime(2001, 1, 1, 0), "*"],
]
patientItems = DBUtil.execute(self.patientItemQuery)
self.assertEqualPatientItems(expectedPatientItems, patientItems)
expectedAssociationStats = \
[
[-11,-11, 3, 3, 3, 3, 4, 999.0, 9999.0, 2, 2, 2, 2, 2, 999.0, 9999.0],
[-11, -6, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0],
[ -6,-11, 1, 1, 1, 2, 2, 172800.0, 29859840000.0, 1, 1, 1, 2, 2, 172800.0, 29859840000.0],
[ -6, -6, 2, 2, 2, 2, 2, 0.0, 0.0, 2, 2, 2, 2, 2, 0.0, 0.0],
]
associationStats = DBUtil.execute(self.clinicalItemAssociationQuery)
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
def test_compositeRelated(self):
# Simulate command-line execution
self.analyzer.main([
"medinfo/cpoe/DataManager.py", "-c",
"-2,-4,-8|NewComposite|New Composite Item|-1|-100"
])
#compositeId = self.analyzer.compositeRelated( (-2,-4,-8), "NewComposite","New Composite Item", -1, -100 );
# Revise the new item ID to a sentinel test value
expectedClinicalItemStatus = \
[
[-1, "CBC",1, 1],
[-2, "BMP",0, 1],
[-3, "Hepatic Panel",1, 1],
[-4, "Cardiac Enzymes",1, 1],
[-5, "CXR",1, 1],
[-6, "RUQ Ultrasound",1, 1],
[-7, "CT Abdomen/Pelvis",1, 1],
[-8, "CT PE Thorax",1, 1],
[-9, "Acetaminophen",1, 1],
[-10, "Carvedilol",1, 1],
[-11, "Enoxaparin",1, 1],
[-12, "Warfarin",1, 1],
[-13, "Ceftriaxone",1, 1],
[-14, "Foley Catheter",1, 1],
[-15, "Strict I&O",1, 1],
[-16, "Fall Precautions",1, 1],
[-100,"NewComposite", 1, 0], # Remove from default recommend list
]
clinicalItemStatus = DBUtil.execute(self.clinicalItemQuery)
self.assertEqualTable(expectedClinicalItemStatus, clinicalItemStatus)
expectedPatientItems = \
[ # Use placeholder "*" for analyze date, just verify that it exists and is consistent. Actual value is not important
# Likewise, use None for primary ID key whose specific value is unimportant
[None,-11111,-100, datetime(2000, 1, 1, 0), None],
[-1, -11111, -4, datetime(2000, 1, 1, 0), "*"],
[-2, -11111, -10, datetime(2000, 1, 1, 0), "*"],
[None,-11111,-100, datetime(2000, 1, 1, 2), None],
[-3, -11111, -8, datetime(2000, 1, 1, 2), "*"],
[-4, -11111, -10, datetime(2000, 1, 2, 0), "*"],
[-5, -11111, -12, datetime(2000, 2, 1, 0), "*"],
[-10, -22222, -7, datetime(2000, 1, 5, 0), "*"],
[-12, -22222, -6, datetime(2000, 1, 9, 0), "*"],
[-13, -22222, -11, datetime(2000, 1, 9, 0), "*"],
[-14, -33333, -6, datetime(2000, 2, 9, 0), "*"],
[None,-33333,-100, datetime(2000, 2,11, 0), None],
[-15, -33333, -2, datetime(2000, 2,11, 0), "*"],
[-16, -33333, -11, datetime(2000, 2,11, 0), "*"],
[-17, -33333, -11, datetime(2001, 1, 1, 0), "*"],
]
patientItems = DBUtil.execute(self.patientItemQuery)
self.assertEqualPatientItems(expectedPatientItems, patientItems)
# Check for tracking link records
linkQuery = \
"""
select
clinical_item_id, linked_item_id
from
clinical_item_link
where
clinical_item_id < 0
order by
clinical_item_id desc, linked_item_id desc
"""
expectedItems = \
[
[-100,-2],
[-100,-4],
[-100,-8],
]
actualItems = DBUtil.execute(linkQuery)
self.assertEqualTable(expectedItems, actualItems)
log.debug("Test incremental update via command-line")
self.analyzer.main(["medinfo/cpoe/DataManager.py", "-g", "-6|-100"])
#self.analyzer.generatePatientItemsForCompositeId( (-6,), -100 );
expectedPatientItems = \
[ # Use placeholder "*" for analyze date, just verify that it exists and is consistent. Actual value is not important
# Likewise, use None for primary ID key whose specific value is unimportant
[None,-11111,-100, datetime(2000, 1, 1, 0), None],
[-1, -11111, -4, datetime(2000, 1, 1, 0), "*"],
[-2, -11111, -10, datetime(2000, 1, 1, 0), "*"],
[None,-11111,-100, datetime(2000, 1, 1, 2), None],
[-3, -11111, -8, datetime(2000, 1, 1, 2), "*"],
[-4, -11111, -10, datetime(2000, 1, 2, 0), "*"],
[-5, -11111, -12, datetime(2000, 2, 1, 0), "*"],
[-10, -22222, -7, datetime(2000, 1, 5, 0), "*"],
[None,-22222,-100, datetime(2000, 1, 9, 0), None],
[-12, -22222, -6, datetime(2000, 1, 9, 0), "*"],
[-13, -22222, -11, datetime(2000, 1, 9, 0), "*"],
[None,-33333,-100, datetime(2000, 2, 9, 0), None],
[-14, -33333, -6, datetime(2000, 2, 9, 0), "*"],
[None,-33333,-100, datetime(2000, 2,11, 0), None],
[-15, -33333, -2, datetime(2000, 2,11, 0), "*"],
[-16, -33333, -11, datetime(2000, 2,11, 0), "*"],
[-17, -33333, -11, datetime(2001, 1, 1, 0), "*"],
]
patientItems = DBUtil.execute(self.patientItemQuery)
self.assertEqualPatientItems(expectedPatientItems, patientItems)
# Check for tracking link records
expectedItems = \
[
[-100,-2],
[-100,-4],
[-100,-6],
[-100,-8],
]
actualItems = DBUtil.execute(linkQuery)
self.assertEqualTable(expectedItems, actualItems)
log.debug("Test inherited update")
self.analyzer.main([
"medinfo/cpoe/DataManager.py", "-c",
"-7,-100|InheritingComposite|Inheriting Composite Item|-1|-101"
])
#compositeId = self.analyzer.compositeRelated( (-7,-100), "InheritingComposite","Inheriting Composite Item", -1, -101 );
# Revise the new item ID to a sentinel test value
expectedClinicalItemStatus = \
[
[-1, "CBC",1, 1],
[-2, "BMP",0, 1],
[-3, "Hepatic Panel",1, 1],
[-4, "Cardiac Enzymes",1, 1],
[-5, "CXR",1, 1],
[-6, "RUQ Ultrasound",1, 1],
[-7, "CT Abdomen/Pelvis",1, 1],
[-8, "CT PE Thorax",1, 1],
[-9, "Acetaminophen",1, 1],
[-10, "Carvedilol",1, 1],
[-11, "Enoxaparin",1, 1],
[-12, "Warfarin",1, 1],
[-13, "Ceftriaxone",1, 1],
[-14, "Foley Catheter",1, 1],
[-15, "Strict I&O",1, 1],
[-16, "Fall Precautions",1, 1],
[-100,"NewComposite", 1, 0],
[-101,"InheritingComposite", 1, 0],
]
clinicalItemStatus = DBUtil.execute(self.clinicalItemQuery)
self.assertEqualTable(expectedClinicalItemStatus, clinicalItemStatus)
expectedPatientItems = \
[ # Use placeholder "*" for analyze date, just verify that it exists and is consistent. Actual value is not important
# Likewise, use None for primary ID key whose specific value is unimportant
[None,-11111,-101, datetime(2000, 1, 1, 0), None],
[None,-11111,-100, datetime(2000, 1, 1, 0), None],
[-1, -11111, -4, datetime(2000, 1, 1, 0), "*"],
[-2, -11111, -10, datetime(2000, 1, 1, 0), "*"],
[None,-11111,-101, datetime(2000, 1, 1, 2), None],
[None,-11111,-100, datetime(2000, 1, 1, 2), None],
[-3, -11111, -8, datetime(2000, 1, 1, 2), "*"],
[-4, -11111, -10, datetime(2000, 1, 2, 0), "*"],
[-5, -11111, -12, datetime(2000, 2, 1, 0), "*"],
[None,-22222,-101, datetime(2000, 1, 5, 0), None],
[-10, -22222, -7, datetime(2000, 1, 5, 0), "*"],
[None,-22222,-101, datetime(2000, 1, 9, 0), None],
[None,-22222,-100, datetime(2000, 1, 9, 0), None],
[-12, -22222, -6, datetime(2000, 1, 9, 0), "*"],
[-13, -22222, -11, datetime(2000, 1, 9, 0), "*"],
[None,-33333,-101, datetime(2000, 2, 9, 0), None],
[None,-33333,-100, datetime(2000, 2, 9, 0), None],
[-14, -33333, -6, datetime(2000, 2, 9, 0), "*"],
[None,-33333,-101, datetime(2000, 2,11, 0), None],
[None,-33333,-100, datetime(2000, 2,11, 0), None],
[-15, -33333, -2, datetime(2000, 2,11, 0), "*"],
[-16, -33333, -11, datetime(2000, 2,11, 0), "*"],
[-17, -33333, -11, datetime(2001, 1, 1, 0), "*"],
]
patientItems = DBUtil.execute(self.patientItemQuery)
self.assertEqualPatientItems(expectedPatientItems, patientItems)
# Check for tracking link records
expectedItems = \
[
[-100,-2],
[-100,-4],
[-100,-6],
[-100,-8],
[-101,-7],
[-101,-100],
]
actualItems = DBUtil.execute(linkQuery)
self.assertEqualTable(expectedItems, actualItems)
def test_mergeRelated(self):
self.analyzer.mergeRelated(-6, (-7, -2))
expectedClinicalItemStatus = \
[
[-1, "CBC",1, 1],
[-2, "BMP",0, 1],
[-3, "Hepatic Panel",1, 1],
[-4, "Cardiac Enzymes",1, 1],
[-5, "CXR",1, 1],
[-6, "RUQ Ultrasound+BMP+CT Abdomen/Pelvis",1, 1],
[-7, "CT Abdomen/Pelvis",0, 1],
[-8, "CT PE Thorax",1, 1],
[-9, "Acetaminophen",1, 1],
[-10, "Carvedilol",1, 1],
[-11, "Enoxaparin",1, 1],
[-12, "Warfarin",1, 1],
[-13, "Ceftriaxone",1, 1],
[-14, "Foley Catheter",1, 1],
[-15, "Strict I&O",1, 1],
[-16, "Fall Precautions",1, 1],
]
clinicalItemStatus = DBUtil.execute(self.clinicalItemQuery)
self.assertEqualTable(expectedClinicalItemStatus, clinicalItemStatus)
expectedPatientItems = \
[ # Use placeholder "*" for analyze date, just verify that it exists and is consistent. Actual value is not important
[-1, -11111, -4, datetime(2000, 1, 1, 0), "*"],
[-2, -11111, -10, datetime(2000, 1, 1, 0), "*"],
[-3, -11111, -8, datetime(2000, 1, 1, 2), "*"],
[-4, -11111, -10, datetime(2000, 1, 2, 0), "*"],
[-5, -11111, -12, datetime(2000, 2, 1, 0), "*"],
[-10, -22222, -6, datetime(2000, 1, 5, 0), None], # Reassign
[-12, -22222, -6, datetime(2000, 1, 9, 0), "*"],
[-13, -22222, -11, datetime(2000, 1, 9, 0), "*"],
[-14, -33333, -6, datetime(2000, 2, 9, 0), "*"],
[-15, -33333, -6, datetime(2000, 2,11, 0), None], # Reassign
[-16, -33333, -11, datetime(2000, 2,11, 0), "*"],
[-17, -33333, -11, datetime(2001, 1, 1, 0), "*"],
]
patientItems = DBUtil.execute(self.patientItemQuery)
self.assertEqualPatientItems(expectedPatientItems, patientItems)
expectedAssociationStats = \
[
[-11,-11, 3, 3, 3, 3, 4, 999.0, 9999.0, 2, 2, 2, 2, 2, 999.0, 9999.0],
[-11, -6, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0],
[ -6,-11, 1, 1, 1, 2, 2, 172800.0, 29859840000.0, 1, 1, 1, 2, 2, 172800.0, 29859840000.0],
[ -6, -6, 2, 2, 2, 2, 2, 0.0, 0.0, 2, 2, 2, 2, 2, 0.0, 0.0],
]
associationStats = DBUtil.execute(self.clinicalItemAssociationQuery)
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
# Check for backup of lost data
backupQuery = \
"""
select
patient_item_id, clinical_item_id
from
backup_link_patient_item
where
clinical_item_id < 0
order by
patient_item_id desc, clinical_item_id
"""
expectedBackupItems = \
[
[-10,-7],
[-15,-2],
]
backupItems = DBUtil.execute(backupQuery)
self.assertEqualTable(expectedBackupItems, backupItems)
def test_unifyRedundant(self):
self.analyzer.unifyRedundant(-7, (-7, -2))
expectedClinicalItemStatus = \
[
[-1, "CBC",1, 1],
[-2, "BMP",0, 1],
[-3, "Hepatic Panel",1, 1],
[-4, "Cardiac Enzymes",1, 1],
[-5, "CXR",1, 1],
[-6, "RUQ Ultrasound",1, 1],
[-7, "CT Abdomen/Pelvis+BMP",1, 1],
[-8, "CT PE Thorax",1, 1],
[-9, "Acetaminophen",1, 1],
[-10, "Carvedilol",1, 1],
[-11, "Enoxaparin",1, 1],
[-12, "Warfarin",1, 1],
[-13, "Ceftriaxone",1, 1],
[-14, "Foley Catheter",1, 1],
[-15, "Strict I&O",1, 1],
[-16, "Fall Precautions",1, 1],
]
clinicalItemStatus = DBUtil.execute(self.clinicalItemQuery)
self.assertEqualTable(expectedClinicalItemStatus, clinicalItemStatus)
expectedPatientItems = \
[ # Use placeholder "*" for analyze date, just verify that it exists and is consistent. Actual value is not important
[-1, -11111, -4, datetime(2000, 1, 1, 0), "*"],
[-2, -11111, -10, datetime(2000, 1, 1, 0), "*"],
[-3, -11111, -8, datetime(2000, 1, 1, 2), "*"],
[-4, -11111, -10, datetime(2000, 1, 2, 0), "*"],
[-5, -11111, -12, datetime(2000, 2, 1, 0), "*"],
[-10, -22222, -7, datetime(2000, 1, 5, 0), "*"],
[-12, -22222, -6, datetime(2000, 1, 9, 0), "*"],
[-13, -22222, -11, datetime(2000, 1, 9, 0), "*"],
[-14, -33333, -6, datetime(2000, 2, 9, 0), "*"],
[-15, -33333, -2, datetime(2000, 2,11, 0), None],
[-16, -33333, -11, datetime(2000, 2,11, 0), "*"],
[-17, -33333, -11, datetime(2001, 1, 1, 0), "*"],
]
patientItems = DBUtil.execute(self.patientItemQuery)
self.assertEqualPatientItems(expectedPatientItems, patientItems)
expectedAssociationStats = \
[
[-11,-11, 3, 3, 3, 3, 4, 999.0, 9999.0, 2, 2, 2, 2, 2, 999.0, 9999.0],
[-11, -7, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0],
[-11, -6, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0],
[ -7,-11, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0, 0, 0, 1, 1, 345600.0, 119439360000.0],
[ -7, -7, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0],
[ -7, -6, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0, 0, 0, 1, 1, 345600.0, 119439360000.0],
[ -6,-11, 1, 1, 1, 2, 2, 172800.0, 29859840000.0, 1, 1, 1, 2, 2, 172800.0, 29859840000.0],
[ -6, -7, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0],
[ -6, -6, 2, 2, 2, 2, 2, 0.0, 0.0, 2, 2, 2, 2, 2, 0.0, 0.0],
]
associationStats = DBUtil.execute(self.clinicalItemAssociationQuery)
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
def assertEqualPatientItems(self, expectedPatientItems, patientItems):
"""Patch the expected items to look for whatever is the set analyze_date,
and just adjust so expect will be present and consistent. Don't care about specific value.
Likewise, don't care about primary key patient_item_id new values
"""
expectedAnalyzeDate = None
for row in patientItems:
analyzeDate = row[-1]
if analyzeDate is not None:
expectedAnalyzeDate = analyzeDate
break
for row in expectedPatientItems:
if expectedAnalyzeDate is not None and row[-1] is not None:
row[-1] = expectedAnalyzeDate
for (expectedRow, actualRow) in zip(expectedPatientItems,
patientItems):
if expectedRow[0] is None:
expectedRow[0] = actualRow[0]
self.assertEqualTable(expectedPatientItems, patientItems)
def test_updateClinicalItemCounts(self):
self.analyzer.updateClinicalItemCounts()
clinicalItemQueryClinicalCounts = \
"""
select
clinical_item_id, name, analysis_status, item_count, patient_count, patient_count, encounter_count
from
clinical_item
where
clinical_item_id < 0
order by
clinical_item_id desc
"""
# Expect counts to default to zero if no values known
expectedClinicalItemCounts = \
[
[-1, "CBC",1, 0, 0, 0, 0],
[-2, "BMP",0, 1, 1, 1, 1],
[-3, "Hepatic Panel",1, 0, 0, 0, 0],
[-4, "Cardiac Enzymes",1, 0, 0, 0, 0],
[-5, "CXR",1, 0, 0, 0, 0],
[-6, "RUQ Ultrasound",1, 2, 2, 2, 2],
[-7, "CT Abdomen/Pelvis",1, 1, 1, 1, 1],
[-8, "CT PE Thorax",1, 0, 0, 0, 0],
[-9, "Acetaminophen",1, 0, 0, 0, 0],
[-10, "Carvedilol",1, 0, 0, 0, 0],
[-11, "Enoxaparin",1, 3, 2, 2, 2], # Two instances occur for the same patient
[-12, "Warfarin",1, 0, 0, 0, 0],
[-13, "Ceftriaxone",1, 0, 0, 0, 0],
[-14, "Foley Catheter",1, 0, 0, 0, 0],
[-15, "Strict I&O",1, 0, 0, 0, 0],
[-16, "Fall Precautions",1, 0, 0, 0, 0],
]
clinicalItemCounts = DBUtil.execute(
clinicalItemQueryClinicalCounts
) #Queries test DB to see what is stored in there
self.assertEqualTable(expectedClinicalItemCounts, clinicalItemCounts)
def test_resetAssociationModel(self):
self.analyzer.updateClinicalItemCounts()
# Generate clinical item counts based on patient item data
ciaCount = DBUtil.execute(
"select count(*) from clinical_item_association")[0][0]
piCount = DBUtil.execute("select count(*) from patient_item")[0][0]
piAnalyzedCount = DBUtil.execute(
"select count(*) from patient_item where analyze_date is not null"
)[0][0]
cacheCount = DBUtil.execute(
"select count(*) from data_cache where data_key in ('analyzedPatientCount')"
)[0][0]
itemCountSummary = DBUtil.execute(
"select sum(item_count) from clinical_item")[0][0]
self.assertTrue(ciaCount > 0)
self.assertTrue(piCount > 0)
self.assertTrue(piAnalyzedCount > 0)
#self.assertTrue(cacheCount > 0);
self.assertTrue(itemCountSummary > 0)
self.analyzer.resetAssociationModel()
ciaCount2 = DBUtil.execute(
"select count(*) from clinical_item_association")[0][0]
piCount2 = DBUtil.execute("select count(*) from patient_item")[0][0]
piAnalyzedCount2 = DBUtil.execute(
"select count(*) from patient_item where analyze_date is not null"
)[0][0]
cacheCount2 = DBUtil.execute(
"select count(*) from data_cache where data_key in ('analyzedPatientCount')"
)[0][0]
itemCountSummary2 = DBUtil.execute(
"select sum(item_count) from clinical_item")[0][0]
self.assertEqual(0, ciaCount2)
self.assertEqual(piCount, piCount2)
self.assertEqual(0, piAnalyzedCount2)
self.assertEqual(0, cacheCount2)
self.assertEqual(0, itemCountSummary2)
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
log.info("Populate the database with test data")
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = [
"clinical_item_id", "clinical_item_category_id", "name",
"analysis_status"
]
dataModels = \
[
RowItemModel( [-1, -1, "CBC",1], headers ),
RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
RowItemModel( [-5, -2, "CXR",1], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
RowItemModel( [-10, -3, "Carvedilol",1], headers ),
RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
RowItemModel( [-12, -3, "Warfarin",1], headers ),
RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
RowItemModel( [-15, -4, "Strict I&O",1], headers ),
RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
self.clinicalItemQuery = \
"""
select
clinical_item_id, name, analysis_status, default_recommend
from
clinical_item
where
clinical_item_id < 0
order by
clinical_item_id desc
"""
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-4, -11111, -10, datetime(2000, 1, 2, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-10, -22222, -7, datetime(2000, 1, 5, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 9, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -11, datetime(2000, 1, 9, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-14, -33333, -6, datetime(2000, 2, 9, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-15, -33333, -2, datetime(2000, 2,11, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-16, -33333, -11, datetime(2000, 2,11, 0), datetime(2100, 1, 1, 0)], headers ),
RowItemModel( [-17, -33333, -11, datetime(2001, 1, 1, 0), datetime(2100, 1, 1, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
self.patientItemQuery = \
"""
select
patient_item_id, patient_id, clinical_item_id, item_date, analyze_date
from
patient_item
where
clinical_item_id < 0
order by
patient_id desc, item_date, patient_item_id desc
"""
headers = [ "clinical_item_id","subsequent_item_id",\
"count_0","count_3600","count_86400","count_604800","count_any",
"time_diff_sum","time_diff_sum_squares",
"patient_count_0","patient_count_3600","patient_count_86400","patient_count_604800","patient_count_any",
"patient_time_diff_sum","patient_time_diff_sum_squares",
"patient_count_0","encounter_count_0",
]
dataModels = \
[
RowItemModel( [-11,-11, 3, 3, 3, 3, 4, 999.0, 9999.0, 2, 2, 2, 2, 2, 999.0, 9999.0, 2,2], headers ),
RowItemModel( [-11, -7, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0,0], headers ),
RowItemModel( [-11, -6, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [-11, -2, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -7,-11, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0,0], headers ),
RowItemModel( [ -7, -7, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -7, -6, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0, 0, 0, 1, 1, 345600.0, 119439360000.0, 0,0], headers ),
RowItemModel( [ -6,-11, 1, 1, 1, 2, 2, 172800.0, 29859840000.0, 1, 1, 1, 2, 2, 172800.0, 29859840000.0, 1,1], headers ),
RowItemModel( [ -6, -7, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0,0], headers ),
RowItemModel( [ -6, -6, 2, 2, 2, 2, 2, 0.0, 0.0, 2, 2, 2, 2, 2, 0.0, 0.0, 2,2], headers ),
RowItemModel( [ -6, -2, 0, 0, 0, 1, 1, 172800.0, 29859840000.0, 0, 0, 0, 1, 1, 172800.0, 29859840000.0, 0,0], headers ),
RowItemModel( [ -2,-11, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -2, -7, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
RowItemModel( [ -2, -6, 0, 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0,0], headers ),
RowItemModel( [ -2, -2, 1, 1, 1, 1, 1, 0.0, 0.0, 1, 1, 1, 1, 1, 0.0, 0.0, 1,1], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
self.clinicalItemAssociationQuery = \
"""
select
clinical_item_id, subsequent_item_id,
count_0,count_3600,count_86400,count_604800,count_any,
time_diff_sum,time_diff_sum_squares,
patient_count_0,patient_count_3600,patient_count_86400,patient_count_604800,patient_count_any,
patient_time_diff_sum, patient_time_diff_sum_squares
from
clinical_item_association
where
clinical_item_id < 0
order by
clinical_item_id, subsequent_item_id
"""
self.analyzer = DataManager()
# Instance to test on
self.analyzer.maxClinicalItemId = 0
class TestItemRecommender(DBTestCase):
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
log.info("Populate the database with test data")
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = ["clinical_item_id", "clinical_item_category_id", "name"]
dataModels = \
[
RowItemModel( [-1, -1, "CBC"], headers ),
RowItemModel( [-2, -1, "BMP"], headers ),
RowItemModel( [-3, -1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, "CXR"], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, "Carvedilol"], headers ),
RowItemModel( [-11, -3, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, "Warfarin"], headers ),
RowItemModel( [-13, -3, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, "Strict I&O"], headers ),
RowItemModel( [-16, -4, "Fall Precautions"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-4, -11111, -10, datetime(2000, 1, 2, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-10, -22222, -7, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -11, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-14, -33333, -6, datetime(2000, 2, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-15, -33333, -2, datetime(2000, 2,11, 0), datetime(2010, 1, 1, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = \
[ "clinical_item_id","subsequent_item_id",
"patient_count_0","patient_count_3600","patient_count_86400","patient_count_604800","patient_count_any",
"time_diff_sum", "time_diff_sum_squares",
]
dataModels = \
[
RowItemModel( [ -1, -1, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -2, -2, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -3, -3, 95, 95, 97, 97, 97, 0.0, 0.0], headers ),
RowItemModel( [ -4, -4, 40, 40, 40, 40, 40, 0.0, 0.0], headers ),
RowItemModel( [ -5, -5, 40, 40, 50, 50, 50, 0.0, 0.0], headers ),
RowItemModel( [ -6, -6, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -2, -3, 0, 0, 0, 0, 0, 0.0, 0.0], headers ), # Zero count associations, probably shouldn't even be here. If so, ignore them anyway
RowItemModel( [ -2, -4, 0, 2, 3, 3, 3, 200.0, 50000.0], headers ),
RowItemModel( [ -2, -6, 2, 2, 5, 5, 5, 300.0, 11990.0], headers ),
RowItemModel( [ -3, -1, 20, 23, 23, 23, 23, 400.0, 344990.0], headers ),
RowItemModel( [ -4, -5, 3, 3, 13, 43, 43, 340.0, 343110.0], headers ),
RowItemModel( [ -4, -6, 23, 33, 33, 33, 63, 420.0, 245220.0], headers ),
RowItemModel( [ -4, -7, 23, 33, 33, 33, 63, 40.0, 5420.0], headers ),
RowItemModel( [ -5, -4, 0, 0, 20, 20, 20, 540.0, 54250.0], headers ),
RowItemModel( [ -6, -2, 7, 7, 7, 7, 7, 1.0, 1.0], headers ),
RowItemModel( [ -6, -4, 20, 20, 20, 20, 20, 1.0, 1.0], headers ),
]
for dataModel in dataModels:
# Add non patient_count variations (Adding 5 to values that are >5 and not for the zero time interval)
for header in headers:
if header.startswith("patient_count_"):
timeStr = header[len("patient_count_"):]
dataModel["count_%s" % timeStr] = dataModel[header]
# Copy over value
if timeStr != "0" and dataModel[header] > 5:
dataModel["count_%s" % timeStr] += 5
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
# Indicate that cache data needs to be updated
self.dataManager = DataManager()
self.dataManager.clearCacheData("analyzedPatientCount")
self.dataManager.clearCacheData("clinicalItemCountsUpdated")
self.recommender = ItemAssociationRecommender()
# Instance to test on
def tearDown(self):
"""Restore state from any setUp or test steps"""
log.info("Purge test records from the database")
DBUtil.execute(
"delete from clinical_item_association where clinical_item_id < 0")
DBUtil.execute("delete from patient_item where patient_item_id < 0")
DBUtil.execute("delete from clinical_item where clinical_item_id < 0")
DBUtil.execute(
"delete from clinical_item_category where clinical_item_category_id in (%s)"
% str.join(",", self.clinicalItemCategoryIdStrList))
DBTestCase.tearDown(self)
def test_recommender(self):
# Run the recommender against the mock test data above and verify expected stats afterwards.
query = RecommenderQuery()
#query.queryItemIds = set();
#query.excludeItemIds = set();
#query.categoryIds = set();
#query.timeDeltaMax = None; # If set to one of the constants (DELTA_ZERO, DELTA_HOUR, etc.), will count item associations that occurred within that time delta as co-occurrent. If left blank, will just consider all items within a given patient as co-occurrent.
query.limit = 3
# Just get top 3 ranks for simplicity
query.maxRecommendedId = 0
# Artificial constraint to focus only on test data
log.debug(
"Query with no item key input, just return ranks by general likelihood then."
)
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-3], headers ),
RowItemModel( [-6], headers ),
RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"Query with key item inputs for which no data exists. Effecitvely ignore it then, so just return ranks by general likelihood."
)
query.queryItemIds = set([-100])
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-3], headers ),
RowItemModel( [-6], headers ),
RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug("Query with category filter on recommended results.")
query.queryItemIds = set([-100])
query.excludeCategoryIds = set([-1, -4, -5, -6])
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-6], headers ),
RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"Query with category filter and specific exclusion filter on recommended results."
)
query.queryItemIds = set([-100])
query.excludeItemIds = set([-6])
query.excludeCategoryIds = set([-1, -4, -5, -6])
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"General query with a couple of input clinical items + one with no association data (should effectively be ignored)."
)
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set()
query.excludeCategoryIds = set()
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-4], headers ),
RowItemModel( [-6], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"General query but set a limit on time delta worth counting item associations"
)
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set()
query.excludeCategoryIds = set()
query.timeDeltaMax = DELTA_HOUR
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-6], headers ),
RowItemModel( [-4], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug("General query with category limit")
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set()
query.excludeCategoryIds = set([-2, -4, -5, -6])
query.timeDeltaMax = DELTA_HOUR
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-4], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug("General query with specific exclusion")
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set([-4, -3, -2])
query.excludeCategoryIds = set()
query.timeDeltaMax = DELTA_HOUR
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-6], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
def test_recommender_aggregation(self):
# Test different scoring aggregation methods
query = RecommenderQuery()
query.countPrefix = "patient_"
query.queryItemIds = set([-2, -5])
#query.excludeItemIds = set();
#query.categoryIds = set();
#query.timeDeltaMax = None; # If set to one of the constants (DELTA_ZERO, DELTA_HOUR, etc.), will count item associations that occurred within that time delta as co-occurrent. If left blank, will just consider all items within a given patient as co-occurrent.
query.limit = 3
# Just get top 3 ranks for simplicity
query.maxRecommendedId = 0
# Artificial constraint to focus only on test data
headers = ["clinical_item_id", "conditionalFreq", "freqRatio"]
# Default weighted aggregation method
expectedData = \
[ RowItemModel( [-4, 0.3, 22.5], headers ),
RowItemModel( [-6, 0.16667, 7.142857], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
# Change to unweighted aggregation method
query.aggregationMethod = "unweighted"
expectedData = \
[ RowItemModel( [-4, 0.32857, 24.64286], headers ),
RowItemModel( [-6, 0.16667, 7.142857], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
# Change to Serial Bayes aggregation method
query.aggregationMethod = "SerialBayes"
expectedData = \
[ RowItemModel( [-4, 0.89157, 66.867471], headers ),
RowItemModel( [-6, 0.16667, 7.142857], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
# Naive Bayes aggregation
query.aggregationMethod = "NaiveBayes"
expectedData = \
[ RowItemModel( [-4, 3.75, 281.25], headers ), # Without truncating negative values
#RowItemModel( [-4, 0.8, 58.59707], headers ), # With truncating negative values
RowItemModel( [-6, 0.16667, 7.142857], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
# Apply value filter
query.fieldFilters["freqRatio>"] = 10.0
expectedData = \
[ RowItemModel( [-6, 0.16667, 7.142857], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
def assertEqualRecommendedData(self, expectedData, recommendedData, query):
"""Run assertEqualGeneral on the key components of the contents of the recommendation data.
Don't necessarily care about the specific numbers that come out of the recommendations,
but do care about consistency in rankings and relative order by the query.sortField
"""
lastScore = None
for expectedItem, recommendedItem in zip(expectedData,
recommendedData):
# Ensure derived statistics are populated to enable comparisons
ItemAssociationRecommender.populateDerivedStats(
recommendedItem, expectedItem.keys())
self.assertEqualDict(expectedItem, recommendedItem,
["clinical_item_id"])
for key in expectedItem.iterkeys(
): # If specified, then verify a specific values
if isinstance(expectedItem[key], float):
self.assertAlmostEquals(expectedItem[key],
recommendedItem[key], 5)
else:
self.assertEqual(expectedItem[key], recommendedItem[key])
if lastScore is not None:
self.assertTrue(recommendedItem[query.sortField] <= lastScore)
# Verify descending order of scores
lastScore = recommendedItem[query.sortField]
self.assertEqual(len(expectedData), len(recommendedData))
def test_recommender_stats(self):
# Run the recommender against the mock test data above and verify expected stats calculations
query = RecommenderQuery()
query.parseParams \
( { "countPrefix": "patient_",
"queryItemIds": "-6",
"resultCount": "3", # Just get top 3 ranks for simplicity
"maxRecommendedId": "0", # Artificial constraint to focus only on test data
"sortField": "P-Fisher", # Specifically request derived expected vs. observed stats
}
)
log.debug("Query with single item not perturbed by others.")
headers = [
"clinical_item_id", "N", "nB", "nA", "nAB", "conditionalFreq",
"baselineFreq", "freqRatio", "P-Fisher"
]
expectedData = \
[
RowItemModel( [-2, SIMULATED_PATIENT_COUNT, 30.0, 70.0, 7.0, 0.1, 0.0100, 10.0, 3.7e-06], headers ),
RowItemModel( [-4, SIMULATED_PATIENT_COUNT, 40.0, 70.0, 20.0, 0.286, 0.0133, 21.42857, 1.2e-23], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedDataStats(expectedData, recommendedData,
headers)
log.debug("Query for non-unique counts.")
query.parseParams \
( { "countPrefix": "",
"sortField": "oddsRatio",
}
)
headers = [
"clinical_item_id", "N", "nB", "nA", "nAB", "conditionalFreq",
"baselineFreq", "freqRatio", "oddsRatio"
]
expectedData = \
[ RowItemModel( [-4, SIMULATED_PATIENT_COUNT, 40.0, 70.0, 25.0, 0.35714, 0.01333, 26.7857, 107.96296], headers ),
RowItemModel( [-2, SIMULATED_PATIENT_COUNT, 30.0, 70.0, 12.0, 0.1714, 0.01, 17.1429, 33.47126], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedDataStats(expectedData, recommendedData,
headers)
def assertEqualRecommendedDataStats(self, expectedData, recommendedData,
headers):
"""Run assertEqualGeneral on the key components of the contents of the recommendation data.
In this case, we do want to verify actual score / stat values match
"""
self.assertEqual(len(expectedData), len(recommendedData))
for expectedItem, recommendedItem in zip(expectedData,
recommendedData):
# Ensure the recommendedData has all fields of interest populated / calculated
ItemAssociationRecommender.populateDerivedStats(
recommendedItem, headers)
for header in headers:
expectedValue = expectedItem[header]
recommendedValue = recommendedItem[header]
msg = 'Dicts diff with key (%s). Verify = %s, Sample = %s' % (
header, expectedValue, recommendedValue)
self.assertAlmostEquals(expectedValue, recommendedValue, 3,
msg)
def test_recommender_stats_commandline(self):
# Run the recommender against the mock test data above and verify expected stats calculations
log.debug("Query with single item not perturbed by others.")
headers = [
"clinical_item_id", "N", "nB", "nA", "nAB", "conditionalFreq",
"baselineFreq", "freqRatio", "P-Fisher"
]
expectedData = \
[
RowItemModel( [-2, SIMULATED_PATIENT_COUNT, 30.0, 70.0, 7.0, 0.1, 0.0100, 10.0, 3.7e-06], headers ),
RowItemModel( [-4, SIMULATED_PATIENT_COUNT, 40.0, 70.0, 20.0, 0.286, 0.0133, 21.42857, 1.2e-23], headers ),
]
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"ItemRecommender.py",
"maxRecommendedId=0&queryItemIds=-6&countPrefix=patient_&resultCount=3&sortField=P-Fisher",
"-"
]
self.recommender.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualRecommendedDataStatsTextOutput(expectedData,
textOutput, headers)
log.debug("Query for non-unique counts.")
headers = [
"clinical_item_id", "N", "nB", "nA", "nAB", "conditionalFreq",
"baselineFreq", "freqRatio", "oddsRatio"
]
expectedData = \
[ RowItemModel( [-4, SIMULATED_PATIENT_COUNT, 40.0, 70.0, 25.0, 0.35714, 0.01333, 26.7857, 107.96296], headers ),
RowItemModel( [-2, SIMULATED_PATIENT_COUNT, 30.0, 70.0, 12.0, 0.1714, 0.01, 17.1429, 33.47126], headers ),
]
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"ItemRecommender.py",
"maxRecommendedId=0&queryItemIds=-6&countPrefix=&resultCount=3&sortField=oddsRatio",
"-"
]
self.recommender.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualRecommendedDataStatsTextOutput(expectedData,
textOutput, headers)
def assertEqualRecommendedDataStatsTextOutput(self, expectedData,
textOutput, headers):
"""Run assertEqualGeneral on the key components of the contents of the recommendation data.
In this case, we do want to verify actual score / stat values match
"""
recommendedData = list()
for dataRow in TabDictReader(textOutput):
for key, value in dataRow.iteritems():
if key in headers:
dataRow[key] = float(value)
# Parse into numerical values for comparison
recommendedData.append(dataRow)
self.assertEqualRecommendedDataStats(expectedData, recommendedData,
headers)
def test_dataCache(self):
# Test that repeating queries with cache turned on will not result in extra DB queries
query = RecommenderQuery()
query.countPrefix = "patient_"
query.queryItemIds = set([-2, -5])
#query.excludeItemIds = set();
#query.categoryIds = set();
#query.timeDeltaMax = None; # If set to one of the constants (DELTA_ZERO, DELTA_HOUR, etc.), will count item associations that occurred within that time delta as co-occurrent. If left blank, will just consider all items within a given patient as co-occurrent.
query.limit = 3
# Just get top 3 ranks for simplicity
query.maxRecommendedId = 0
# Artificial constraint to focus only on test data
headers = ["clinical_item_id", "conditionalFreq", "freqRatio"]
# First query without cache
self.recommender.dataManager.dataCache = None
baselineData = self.recommender(query)
baselineQueryCount = self.recommender.dataManager.queryCount
# Redo query with cache
self.recommender.dataManager.dataCache = dict()
newData = self.recommender(query)
newQueryCount = self.recommender.dataManager.queryCount
self.assertEqualRecommendedData(baselineData, newData, query)
# Ensure getting same results
self.assertNotEqual(baselineQueryCount, newQueryCount)
# Expect needed more queries since no prior cache
baselineQueryCount = newQueryCount
# Again, but should be no new query since have cached results last time
newData = self.recommender(query)
newQueryCount = self.recommender.dataManager.queryCount
self.assertEqualRecommendedData(baselineData, newData, query)
self.assertEqual(baselineQueryCount, newQueryCount)
# Repeat multiple times, should still have no new query activity
# prog = ProgressDots(10,1,"repeats");
for iRepeat in xrange(10):
newData = self.recommender(query)
newQueryCount = self.recommender.dataManager.queryCount
self.assertEqualRecommendedData(baselineData, newData, query)
self.assertEqual(baselineQueryCount, newQueryCount)
# prog.update();
# prog.printStatus();
# Query for subset should still yield no new query
query.queryItemIds = set([-2])
newData = self.recommender(query)
newQueryCount = self.recommender.dataManager.queryCount
baselineData = newData
# New baseline for subset
self.assertEqual(baselineQueryCount, newQueryCount)
# Expect no queries for subsets
# Repeat query for subset
newData = self.recommender(query)
newQueryCount = self.recommender.dataManager.queryCount
self.assertEqualRecommendedData(baselineData, newData, query)
self.assertEqual(baselineQueryCount, newQueryCount)
# Expect no queries for subsets
# Query for partial subset, partial new
query.queryItemIds = set([-5, -6])
newData = self.recommender(query)
newQueryCount = self.recommender.dataManager.queryCount
baselineData = newData
# New baseline for subset
self.assertEqual(baselineQueryCount, newQueryCount)
# Expect now new queries for subsets, because first query should have done mass-all query
# Repeat for partial subset, no longer new
newData = self.recommender(query)
newQueryCount = self.recommender.dataManager.queryCount
baselineData = newData
# New baseline for subset
self.assertEqualRecommendedData(baselineData, newData, query)
self.assertEqual(baselineQueryCount, newQueryCount)
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
log.info("Populate the database with test data")
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = ["clinical_item_id", "clinical_item_category_id", "name"]
dataModels = \
[
RowItemModel( [-1, -1, "CBC"], headers ),
RowItemModel( [-2, -1, "BMP"], headers ),
RowItemModel( [-3, -1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, "CXR"], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, "Carvedilol"], headers ),
RowItemModel( [-11, -3, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, "Warfarin"], headers ),
RowItemModel( [-13, -3, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, "Strict I&O"], headers ),
RowItemModel( [-16, -4, "Fall Precautions"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-4, -11111, -10, datetime(2000, 1, 2, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-10, -22222, -7, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -11, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-14, -33333, -6, datetime(2000, 2, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-15, -33333, -2, datetime(2000, 2,11, 0), datetime(2010, 1, 1, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = \
[ "clinical_item_id","subsequent_item_id",
"patient_count_0","patient_count_3600","patient_count_86400","patient_count_604800","patient_count_any",
"time_diff_sum", "time_diff_sum_squares",
]
dataModels = \
[
RowItemModel( [ -1, -1, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -2, -2, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -3, -3, 95, 95, 97, 97, 97, 0.0, 0.0], headers ),
RowItemModel( [ -4, -4, 40, 40, 40, 40, 40, 0.0, 0.0], headers ),
RowItemModel( [ -5, -5, 40, 40, 50, 50, 50, 0.0, 0.0], headers ),
RowItemModel( [ -6, -6, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -2, -3, 0, 0, 0, 0, 0, 0.0, 0.0], headers ), # Zero count associations, probably shouldn't even be here. If so, ignore them anyway
RowItemModel( [ -2, -4, 0, 2, 3, 3, 3, 200.0, 50000.0], headers ),
RowItemModel( [ -2, -6, 2, 2, 5, 5, 5, 300.0, 11990.0], headers ),
RowItemModel( [ -3, -1, 20, 23, 23, 23, 23, 400.0, 344990.0], headers ),
RowItemModel( [ -4, -5, 3, 3, 13, 43, 43, 340.0, 343110.0], headers ),
RowItemModel( [ -4, -6, 23, 33, 33, 33, 63, 420.0, 245220.0], headers ),
RowItemModel( [ -4, -7, 23, 33, 33, 33, 63, 40.0, 5420.0], headers ),
RowItemModel( [ -5, -4, 0, 0, 20, 20, 20, 540.0, 54250.0], headers ),
RowItemModel( [ -6, -2, 7, 7, 7, 7, 7, 1.0, 1.0], headers ),
RowItemModel( [ -6, -4, 20, 20, 20, 20, 20, 1.0, 1.0], headers ),
]
for dataModel in dataModels:
# Add non patient_count variations (Adding 5 to values that are >5 and not for the zero time interval)
for header in headers:
if header.startswith("patient_count_"):
timeStr = header[len("patient_count_"):]
dataModel["count_%s" % timeStr] = dataModel[header]
# Copy over value
if timeStr != "0" and dataModel[header] > 5:
dataModel["count_%s" % timeStr] += 5
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
# Indicate that cache data needs to be updated
self.dataManager = DataManager()
self.dataManager.clearCacheData("analyzedPatientCount")
self.dataManager.clearCacheData("clinicalItemCountsUpdated")
self.recommender = ItemAssociationRecommender()
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
log.info("Populate the database with test data")
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = ["clinical_item_id", "clinical_item_category_id", "name"]
dataModels = \
[
RowItemModel( [-1, -1, "CBC"], headers ),
RowItemModel( [-2, -1, "BMP"], headers ),
RowItemModel( [-3, -1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, "CXR"], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, "Carvedilol"], headers ),
RowItemModel( [-11, -3, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, "Warfarin"], headers ),
RowItemModel( [-13, -3, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, "Strict I&O"], headers ),
RowItemModel( [-16, -4, "Fall Precautions"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 1), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-4, -11111, -10, datetime(2000, 1, 2, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-10, -22222, -7, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -11, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-14, -33333, -6, datetime(2000, 2, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-15, -33333, -2, datetime(2000, 2,11, 0), datetime(2010, 1, 1, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = \
[ "clinical_item_id","subsequent_item_id",
"count_0","count_3600","count_86400","count_604800","count_any",
"time_diff_sum", "time_diff_sum_squares",
]
dataModels = \
[
RowItemModel( [ -1, -1, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -2, -2, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -3, -3, 95, 95, 97, 97, 97, 0.0, 0.0], headers ),
RowItemModel( [ -4, -4, 240,240,240,240,240, 0.0, 0.0], headers ),
RowItemModel( [ -5, -5, 40, 40, 50, 50, 50, 0.0, 0.0], headers ),
RowItemModel( [ -6, -6, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -7, -7, 35, 35, 35, 50, 80, 0.0, 0.0], headers ),
RowItemModel( [ -8, -8, 35, 35, 35, 50, 80, 0.0, 0.0], headers ),
RowItemModel( [-10,-10, 45, 45, 55, 60, 90, 0.0, 0.0], headers ),
RowItemModel( [-12,-12, 75, 75, 75, 80, 90, 0.0, 0.0], headers ),
RowItemModel( [ -2, -4, 0, 2, 3, 3, 3, 200.0, 50000.0], headers ),
RowItemModel( [ -2, -6, 2, 2, 5, 5, 5, 300.0, 11990.0], headers ),
RowItemModel( [ -3, -1, 20, 23, 23, 23, 23, 400.0, 344990.0], headers ),
RowItemModel( [ -4, -5, 3, 3, 13, 43, 43, 340.0, 343110.0], headers ),
RowItemModel( [ -4, -6, 23, 33, 33, 33, 63, 420.0, 245220.0], headers ),
RowItemModel( [ -4, -7, 27, 33, 33, 33, 83, 40.0, 5420.0], headers ),
RowItemModel( [ -4, -8, 1, 2, 3, 4, 5, 40.0, 5420.0], headers ),
RowItemModel( [ -4,-10, 25, 35, 40, 45, 73, 47.0, 5420.0], headers ),
RowItemModel( [ -5, -4, 0, 0, 20, 20, 20, 540.0, 54250.0], headers ),
RowItemModel( [-10, -8, 2, 4, 6, 8, 10, 47.0, 5420.0], headers ),
RowItemModel( [-10, -12, 12, 14, 16, 18, 20, 47.0, 5420.0], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
# Indicate that cache data needs tobe updated
self.dataManager = DataManager()
self.dataManager.clearCacheData("analyzedPatientCount")
self.dataManager.clearCacheData("clinicalItemCountsUpdated")
# Instance to test on
self.analyzer = RecommendationRankingTrendAnalysis()
class TestRecommendationRankingTrendAnalysis(DBTestCase):
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
log.info("Populate the database with test data")
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = ["clinical_item_id", "clinical_item_category_id", "name"]
dataModels = \
[
RowItemModel( [-1, -1, "CBC"], headers ),
RowItemModel( [-2, -1, "BMP"], headers ),
RowItemModel( [-3, -1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, "CXR"], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, "Carvedilol"], headers ),
RowItemModel( [-11, -3, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, "Warfarin"], headers ),
RowItemModel( [-13, -3, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, "Strict I&O"], headers ),
RowItemModel( [-16, -4, "Fall Precautions"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "patient_id", "clinical_item_id", "item_date",
"analyze_date"
]
dataModels = \
[
RowItemModel( [-1, -11111, -4, datetime(2000, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-2, -11111, -10, datetime(2000, 1, 1, 1), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-3, -11111, -8, datetime(2000, 1, 1, 2), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-4, -11111, -10, datetime(2000, 1, 2, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-10, -22222, -7, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-12, -22222, -6, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-13, -22222, -11, datetime(2000, 1, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-14, -33333, -6, datetime(2000, 2, 9, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-15, -33333, -2, datetime(2000, 2,11, 0), datetime(2010, 1, 1, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = \
[ "clinical_item_id","subsequent_item_id",
"count_0","count_3600","count_86400","count_604800","count_any",
"time_diff_sum", "time_diff_sum_squares",
]
dataModels = \
[
RowItemModel( [ -1, -1, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -2, -2, 30, 30, 30, 30, 30, 0.0, 0.0], headers ),
RowItemModel( [ -3, -3, 95, 95, 97, 97, 97, 0.0, 0.0], headers ),
RowItemModel( [ -4, -4, 240,240,240,240,240, 0.0, 0.0], headers ),
RowItemModel( [ -5, -5, 40, 40, 50, 50, 50, 0.0, 0.0], headers ),
RowItemModel( [ -6, -6, 70, 70, 70, 70, 70, 0.0, 0.0], headers ),
RowItemModel( [ -7, -7, 35, 35, 35, 50, 80, 0.0, 0.0], headers ),
RowItemModel( [ -8, -8, 35, 35, 35, 50, 80, 0.0, 0.0], headers ),
RowItemModel( [-10,-10, 45, 45, 55, 60, 90, 0.0, 0.0], headers ),
RowItemModel( [-12,-12, 75, 75, 75, 80, 90, 0.0, 0.0], headers ),
RowItemModel( [ -2, -4, 0, 2, 3, 3, 3, 200.0, 50000.0], headers ),
RowItemModel( [ -2, -6, 2, 2, 5, 5, 5, 300.0, 11990.0], headers ),
RowItemModel( [ -3, -1, 20, 23, 23, 23, 23, 400.0, 344990.0], headers ),
RowItemModel( [ -4, -5, 3, 3, 13, 43, 43, 340.0, 343110.0], headers ),
RowItemModel( [ -4, -6, 23, 33, 33, 33, 63, 420.0, 245220.0], headers ),
RowItemModel( [ -4, -7, 27, 33, 33, 33, 83, 40.0, 5420.0], headers ),
RowItemModel( [ -4, -8, 1, 2, 3, 4, 5, 40.0, 5420.0], headers ),
RowItemModel( [ -4,-10, 25, 35, 40, 45, 73, 47.0, 5420.0], headers ),
RowItemModel( [ -5, -4, 0, 0, 20, 20, 20, 540.0, 54250.0], headers ),
RowItemModel( [-10, -8, 2, 4, 6, 8, 10, 47.0, 5420.0], headers ),
RowItemModel( [-10, -12, 12, 14, 16, 18, 20, 47.0, 5420.0], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_association",
dataModel)
# Indicate that cache data needs tobe updated
self.dataManager = DataManager()
self.dataManager.clearCacheData("analyzedPatientCount")
self.dataManager.clearCacheData("clinicalItemCountsUpdated")
# Instance to test on
self.analyzer = RecommendationRankingTrendAnalysis()
def tearDown(self):
"""Restore state from any setUp or test steps"""
log.info("Purge test records from the database")
DBUtil.execute(
"delete from clinical_item_association where clinical_item_id < 0")
DBUtil.execute("delete from patient_item where patient_item_id < 0")
DBUtil.execute("delete from clinical_item where clinical_item_id < 0")
DBUtil.execute(
"delete from clinical_item_category where clinical_item_category_id in (%s)"
% str.join(",", self.clinicalItemCategoryIdStrList))
DBTestCase.tearDown(self)
def test_recommenderAnalysis(self):
# Run the recommender against the mock test data above and verify expected stats afterwards.
analysisQuery = AnalysisQuery()
analysisQuery.patientIds = set([-11111])
analysisQuery.recommender = BaselineFrequencyRecommender()
#analysisQuery.recommender = ItemAssociationRecommender();
analysisQuery.baseRecQuery = RecommenderQuery()
analysisQuery.baseRecQuery.maxRecommendedId = 0
# Restrict to test data
# Don't use items whose default is to be excluded from recommendations
#recQuery.excludeCategoryIds = recommender.defaultExcludedClinicalItemCategoryIds(conn=conn);
#recQuery.excludeItemIds = recommender.defaultExcludedClinicalItemIds(conn=conn);
#recQuery.timeDeltaMax = timedelta(0, int(self.requestData["timeDeltaMax"]) ); # Time delta to use for queries, otherwise just default to all times
colNames = [
"patient_id", "clinical_item_id", "iItem", "iRecItem", "recRank",
"recScore"
]
# Start with default recommender
expectedResults = \
[
(-11111, -4, 0, 0, 1, SENTINEL_ANY_FLOAT), #0.170), Don't care about specific scores, as long as ranks are correct
(-11111,-10, 1, 1, 4, SENTINEL_ANY_FLOAT), #0.032),
(-11111, -8, 2, 2, 5, SENTINEL_ANY_FLOAT), #0.025),
(-11111,-12, 4, 3, 2, SENTINEL_ANY_FLOAT), #0.053),
]
analysisResults = self.analyzer(analysisQuery)
self.assertEqualTable(expectedResults, analysisResults, 3)
# Now try targeted recommender
analysisQuery.recommender = ItemAssociationRecommender()
expectedResults = \
[ (-11111, -4, 0, 0, 1, SENTINEL_ANY_FLOAT), #0.167),
(-11111,-10, 1, 1, 2, SENTINEL_ANY_FLOAT), #0.304),
(-11111, -8, 2, 2, 5, SENTINEL_ANY_FLOAT), #0.190),
(-11111,-12, 4, 3, 1, SENTINEL_ANY_FLOAT), #0.444),
]
analysisResults = self.analyzer(analysisQuery)
self.assertEqualTable(expectedResults, analysisResults, 3)
# Repeat, but put a limit on maximum number of query items and recommendations we want analyzed
analysisQuery.queryItemMax = 2
expectedResults = \
[ (-11111, -4, 0, 0, 1, SENTINEL_ANY_FLOAT), #0.167),
(-11111,-10, 1, 1, 2, SENTINEL_ANY_FLOAT), #0.304),
]
analysisResults = self.analyzer(analysisQuery)
self.assertEqualTable(expectedResults, analysisResults, 3)
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
log.info("Populate the database with test data")
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = [
"clinical_item_id", "clinical_item_category_id", "name",
"analysis_status"
]
dataModels = \
[
RowItemModel( [-1, -1, "CBC",1], headers ),
RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
RowItemModel( [-5, -2, "CXR",1], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
RowItemModel( [-10, -3, "Carvedilol",1], headers ),
RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
RowItemModel( [-12, -3, "Warfarin",1], headers ),
RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
RowItemModel( [-15, -4, "Strict I&O",1], headers ),
RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "encounter_id", "patient_id",
"clinical_item_id", "item_date"
]
dataModels = \
[
RowItemModel( [-1, -111, -11111, -4, datetime(2000, 1, 1, 0)], headers ),
RowItemModel( [-2, -111, -11111, -10, datetime(2000, 1, 1, 0)], headers ),
RowItemModel( [-3, -111, -11111, -8, datetime(2000, 1, 1, 2)], headers ),
RowItemModel( [-4, -112, -11111, -10, datetime(2000, 1, 2, 0)], headers ),
RowItemModel( [-5, -112, -11111, -12, datetime(2000, 2, 1, 0)], headers ),
RowItemModel( [-10, -222, -22222, -7, datetime(2000, 1, 5, 0)], headers ),
RowItemModel( [-12, -222, -22222, -6, datetime(2000, 1, 9, 0)], headers ),
RowItemModel( [-13, -222, -22222, -11, datetime(2000, 1, 9, 0)], headers ),
RowItemModel( [-95, -222, -22222, -9, datetime(2000, 1,10, 0)], headers ),
RowItemModel( [-94, -333, -33333, -8, datetime(2000, 1,10, 0)], headers ), # In first window delta unit only
RowItemModel( [-14, -333, -33333, -6, datetime(2000, 2, 9, 0)], headers ),
RowItemModel( [-15, -333, -33333, -2, datetime(2000, 2,11, 0)], headers ), # Will set clinical_item_link inheritances to this item to only record certain associations
RowItemModel( [-16, -333, -33333, -11, datetime(2000, 2,11, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = ["clinical_item_id", "linked_item_id"]
dataModels = \
[ # Don't have direct, but instead demonstrate inherited relationship from 6 to 2 will still be recognized
RowItemModel( [-6, -4], headers ),
RowItemModel( [-4, -2], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_link", dataModel)
self.decayAnalyzer = DecayingWindows(
) # DecayingWindows instance to test on, *** remember to change database to medinfo_copy
self.dataManager = DataManager()
class TestDecayingWindows(DBTestCase):
def setUp(self):
"""Prepare state for test cases"""
DBTestCase.setUp(self)
log.info("Populate the database with test data")
from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
ClinicalItemDataLoader.build_clinical_item_psql_schemata()
self.clinicalItemCategoryIdStrList = list()
headers = ["clinical_item_category_id", "source_table"]
dataModels = \
[
RowItemModel( [-1, "Labs"], headers ),
RowItemModel( [-2, "Imaging"], headers ),
RowItemModel( [-3, "Meds"], headers ),
RowItemModel( [-4, "Nursing"], headers ),
RowItemModel( [-5, "Problems"], headers ),
RowItemModel( [-6, "Lab Results"], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_category",
dataModel)
self.clinicalItemCategoryIdStrList.append(str(dataItemId))
headers = [
"clinical_item_id", "clinical_item_category_id", "name",
"analysis_status"
]
dataModels = \
[
RowItemModel( [-1, -1, "CBC",1], headers ),
RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
RowItemModel( [-5, -2, "CXR",1], headers ),
RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
RowItemModel( [-10, -3, "Carvedilol",1], headers ),
RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
RowItemModel( [-12, -3, "Warfarin",1], headers ),
RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
RowItemModel( [-15, -4, "Strict I&O",1], headers ),
RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel)
headers = [
"patient_item_id", "encounter_id", "patient_id",
"clinical_item_id", "item_date"
]
dataModels = \
[
RowItemModel( [-1, -111, -11111, -4, datetime(2000, 1, 1, 0)], headers ),
RowItemModel( [-2, -111, -11111, -10, datetime(2000, 1, 1, 0)], headers ),
RowItemModel( [-3, -111, -11111, -8, datetime(2000, 1, 1, 2)], headers ),
RowItemModel( [-4, -112, -11111, -10, datetime(2000, 1, 2, 0)], headers ),
RowItemModel( [-5, -112, -11111, -12, datetime(2000, 2, 1, 0)], headers ),
RowItemModel( [-10, -222, -22222, -7, datetime(2000, 1, 5, 0)], headers ),
RowItemModel( [-12, -222, -22222, -6, datetime(2000, 1, 9, 0)], headers ),
RowItemModel( [-13, -222, -22222, -11, datetime(2000, 1, 9, 0)], headers ),
RowItemModel( [-95, -222, -22222, -9, datetime(2000, 1,10, 0)], headers ),
RowItemModel( [-94, -333, -33333, -8, datetime(2000, 1,10, 0)], headers ), # In first window delta unit only
RowItemModel( [-14, -333, -33333, -6, datetime(2000, 2, 9, 0)], headers ),
RowItemModel( [-15, -333, -33333, -2, datetime(2000, 2,11, 0)], headers ), # Will set clinical_item_link inheritances to this item to only record certain associations
RowItemModel( [-16, -333, -33333, -11, datetime(2000, 2,11, 0)], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
headers = ["clinical_item_id", "linked_item_id"]
dataModels = \
[ # Don't have direct, but instead demonstrate inherited relationship from 6 to 2 will still be recognized
RowItemModel( [-6, -4], headers ),
RowItemModel( [-4, -2], headers ),
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("clinical_item_link", dataModel)
self.decayAnalyzer = DecayingWindows(
) # DecayingWindows instance to test on, *** remember to change database to medinfo_copy
self.dataManager = DataManager()
def tearDown(self):
"""Restore state from any setUp or test steps"""
log.info("Purge test records from the database")
DBUtil.execute(
"delete from clinical_item_link where clinical_item_id < 0")
DBUtil.execute(
"delete from clinical_item_association where clinical_item_id < 0")
DBUtil.execute("delete from patient_item where patient_item_id < 0")
DBUtil.execute("delete from clinical_item where clinical_item_id < 0")
DBUtil.execute(
"delete from clinical_item_category where clinical_item_category_id in (%s)"
% str.join(",", self.clinicalItemCategoryIdStrList))
# Purge temporary buffer files. May not match exact name if modified for other purpose
for filename in os.listdir("."):
if filename.startswith(TEMP_FILENAME):
os.remove(filename)
DBTestCase.tearDown(self)
def test_decayingWindowsFromBuffer(self):
associationQuery = \
"""
select
clinical_item_id, subsequent_item_id,
count_0, count_3600, count_86400, count_604800,
count_2592000, count_7776000, count_31536000,
count_any
from
clinical_item_association
where
clinical_item_id < 0
order by
clinical_item_id, subsequent_item_id
"""
decayAnalysisOptions = DecayAnalysisOptions()
decayAnalysisOptions.startD = datetime(2000, 1, 9)
decayAnalysisOptions.endD = datetime(2000, 2, 11)
#decayAnalysisOptions.windowLength = 10
decayAnalysisOptions.decay = 0.9
decayAnalysisOptions.delta = timedelta(weeks=4)
decayAnalysisOptions.patientIds = [-22222, -33333]
decayAnalysisOptions.outputFile = TEMP_FILENAME
self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
expectedAssociationStats = \
[
[-11,-11, 1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9], # Note that decaying windows approach will not try to update counts for time periods longer than the delta period
[-11, -9, 0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
[-11, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted. Consider future upgrade. Don't train on all time ever, but train on two deltas at a time, sliding / shifting window so do catch the overlap ranges
[-11, -6, 0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -9,-11, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -9, -9, 0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -9, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -9, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -8,-11, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -8, -9, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -8, -8, 0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -8, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -6,-11, 0.9, 0.9, 0.9, 1.9, 1.9, 0, 0, 1.9],
[ -6, -9, 0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -6, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -6, -6, 1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],
]
associationStats = DBUtil.execute(associationQuery)
#for row in expectedAssociationStats:
# print >> sys.stderr, row;
#print >> sys.stderr, "============"
#for row in associationStats:
# print >> sys.stderr, row;
#print >> sys.stderr, "============"
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
expectedItemBaseCountById = \
{
-1: 0,
-2: 0,
-3: 0,
-4: 0,
-5: 0,
-6: 1.9,
-7: 0,
-8: 0.9,
-9: 0.9,
-10: 0,
-11: 1.9,
-12: 0,
-13: 0,
-14: 0,
-15: 0,
-16: 0,
}
itemBaseCountById = self.dataManager.loadClinicalItemBaseCountByItemId(
)
#print >> sys.stderr, itemBaseCountById;
self.assertEqualDict(expectedItemBaseCountById, itemBaseCountById)
######## Reset the model data and rerun with different decay parameters
self.dataManager.resetAssociationModel()
decayAnalysisOptions = DecayAnalysisOptions()
decayAnalysisOptions.startD = datetime(2000, 1, 9)
decayAnalysisOptions.endD = datetime(2000, 2, 11)
decayAnalysisOptions.windowLength = 4
# Just specify window length, then should calculate decay parameter
#decayAnalysisOptions.decay = 0.9
decayAnalysisOptions.delta = timedelta(weeks=4)
decayAnalysisOptions.patientIds = [-22222, -33333]
decayAnalysisOptions.outputFile = TEMP_FILENAME
self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
expectedAssociationStats = \
[
[-11,-11, 1.75, 1.75, 1.75, 1.75, 1.75, 0, 0, 1.75],
[-11, -9, 0.0, 0.0, 0.75, 0.75, 0.75, 0, 0, 0.75],
[-11, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[-11, -6, 0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
[ -9,-11, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -9, -9, 0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
[ -9, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -9, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -8,-11, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -8, -9, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -8, -8, 0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
[ -8, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -6,-11, 0.75, 0.75, 0.75, 1.75, 1.75, 0, 0, 1.75],
[ -6, -9, 0.0, 0.0, 0.75, 0.75, 0.75, 0, 0, 0.75],
[ -6, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -6, -6, 1.75, 1.75, 1.75, 1.75, 1.75, 0, 0, 1.75],
]
associationStats = DBUtil.execute(associationQuery)
#for row in expectedAssociationStats:
# print >> sys.stderr, row;
#print >> sys.stderr, "============"
#for row in associationStats:
# print >> sys.stderr, row;
#print >> sys.stderr, "============"
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
expectedItemBaseCountById = \
{
-1: 0,
-2: 0,
-3: 0,
-4: 0,
-5: 0,
-6: 1.75,
-7: 0,
-8: 0.75,
-9: 0.75,
-10: 0,
-11: 1.75,
-12: 0,
-13: 0,
-14: 0,
-15: 0,
-16: 0,
}
itemBaseCountById = self.dataManager.loadClinicalItemBaseCountByItemId(
acceptCache=False)
# Don't use cache, otherwise will get prior results
#print >> sys.stderr, itemBaseCountById;
self.assertEqualDict(expectedItemBaseCountById, itemBaseCountById)
def test_decayingWindows(self):
# Muthu's function to test DecayingWindows module
associationQuery = \
"""
select
clinical_item_id, subsequent_item_id,
patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
patient_count_2592000, patient_count_7776000, patient_count_31536000,
patient_count_any
from
clinical_item_association
where
clinical_item_id < 0
order by
clinical_item_id, subsequent_item_id
"""
decayAnalysisOptions = DecayAnalysisOptions()
decayAnalysisOptions.startD = datetime(2000, 1, 9)
decayAnalysisOptions.endD = datetime(2000, 2, 11)
decayAnalysisOptions.windowLength = 10
decayAnalysisOptions.decay = 0.9
decayAnalysisOptions.delta = timedelta(weeks=4)
decayAnalysisOptions.patientIds = [-22222, -33333]
self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
expectedAssociationStats = \
[
[-11,-11, 1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9], # Note that decaying windows approach will not try to update counts for time periods longer than the delta period
[-11, -9, 0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
[-11, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted. Consider future upgrade. Don't train on all time ever, but train on two deltas at a time, sliding / shifting window so do catch the overlap ranges. Problem here is buffer based algorithm, won't be recording analyze_dates as go, so will end up with duplicate counts of items each month?
[-11, -6, 0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -9,-11, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -9, -9, 0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -9, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -9, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
[ -8,-11, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -8, -9, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -8, -8, 0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -8, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -6,-11, 0.9, 0.9, 0.9, 1.9, 1.9, 0, 0, 1.9],
[ -6, -9, 0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
[ -6, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], # 8 not in same delta as other items so co-occurence not gettign counted.
[ -6, -6, 1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],
]
associationStats = DBUtil.execute(associationQuery)
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
#DBUtil.execute("delete from clinical_item_association")
# Add another training period then should get a second decay multiplier for older data?
# Weird in that incrementally building on prior data that is getting decayed, even though new training data actually occurred before chronologic time of data
decayAnalysisOptions = DecayAnalysisOptions()
decayAnalysisOptions.startD = datetime(2000, 1, 1)
decayAnalysisOptions.endD = datetime(2000, 2, 12)
decayAnalysisOptions.windowLength = 10
decayAnalysisOptions.decay = 0.9
decayAnalysisOptions.delta = timedelta(weeks=4)
decayAnalysisOptions.patientIds = [-22222, -33333]
self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
expectedAssociationStats = \
[
[-11L, -11L, 1.539, 1.539, 1.539, 1.539, 1.539, 0.0, 0.0, 1.539],
[-11L, -9L, 0.0, 0.0, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
[-11L, -8L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-11L, -7L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-11L, -6L, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
[-9L, -11L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-9L, -9L, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
[-9L, -8L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-9L, -7L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-9L, -6L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-8L, -11L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-8L, -9L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-8L, -8L, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
[-8L, -6L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-7L, -11L, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
[-7L, -9L, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
[-7L, -7L, 0.9, 0.9, 0.9, 0.9, 0.9, 0.0, 0.0, 0.9],
[-7L, -6L, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
[-6L, -11L, 0.729, 0.729, 0.729, 1.539, 1.539, 0.0, 0.0, 1.539],
[-6L, -9L, 0.0, 0.0, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
[-6L, -8L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-6L, -7L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-6L, -6L, 1.539, 1.539, 1.539, 1.539, 1.539, 0.0, 0.0, 1.539],
]
associationStats = DBUtil.execute(associationQuery)
#for row in expectedAssociationStats:
# print >> sys.stderr, row;
#print >> sys.stderr, "============"
#for row in associationStats:
# print >> sys.stderr, row;
#print >> sys.stderr, "============"
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
def test_resetModel(self):
associationQuery = \
"""
select
clinical_item_id, subsequent_item_id,
patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
patient_count_2592000, patient_count_7776000, patient_count_31536000,
patient_count_any
from
clinical_item_association
where
clinical_item_id < 0
order by
clinical_item_id, subsequent_item_id
"""
associationQueryDate = \
"""
select
patient_item_id, analyze_date
from
patient_item
where
patient_item_id < 0
order by
patient_item_id
"""
# fill up the association table with something
decayAnalysisOptions = DecayAnalysisOptions()
decayAnalysisOptions.startD = datetime(2000, 1, 9)
decayAnalysisOptions.endD = datetime(2000, 2, 11)
decayAnalysisOptions.windowLength = 10
decayAnalysisOptions.decay = 0.9
decayAnalysisOptions.patientIds = [-22222, -33333]
self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
# then clear the table
self.dataManager.resetAssociationModel()
expectedAssociationStats = \
[
]
associationStats = DBUtil.execute(associationQuery)
self.assertEqualTable(expectedAssociationStats,
associationStats,
precision=3)
# Set as NULL
expectedAssociationStatsDate = \
[[-95, None],[-94, None],[-16, None], [-15, None], [-14, None], [-13, None], [-12, None], [-10, None], [-5, None], [-4, None], [-3, None], [-2, None], [-1, None]
]
associationStatsDate = DBUtil.execute(associationQueryDate)
#print >> sys.stderr, associationStatsDate
self.assertEqualTable(expectedAssociationStatsDate,
associationStatsDate)
def __init__(self):
self.connFactory = DBUtil.ConnectionFactory()
# Default connection source
self.dataManager = DataManager()
