def main_prepPatientItems(argv):
prep = PreparePatientItems();
for i in xrange(-9,10):
prep.main(["PreparePatientItems","-S",START_DATE_STR, "-E",END_DATE_STR, "-p",str(DEMOGRAPHICS_CATEGORY_ID),"-c", str(ADMIT_DX_CATEGORY_ID), "-Q", str(QUERY_TIME), "-V", str(VERIFY_TIME), "-o", str(DEATH_ITEM_ID), "-t", "2592000", "%s/patientIds.5year.%s.tab.gz" % (SOURCE_DATA_DIR, i), "%s/firstItems.q%s.v%s.%s.%s.tab.gz" % (SOURCE_DATA_DIR, QUERY_TIME, VERIFY_TIME, BASE_YEAR, i)]);
# Convert to (filtered) Bag of Words
for i in xrange(-9,10):
prep.main(["PreparePatientItems","-B","qvo","-X", EXCLUDE_CATEGORY_IDS_STR,"%s/firstItems.q%s.v%s.%s.%d.tab.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,i),"%s/firstItems.q%s.v%s.%s.%d.filter.bow.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,i)]);
# Concatenate batch of files
ofs = stdOpen("%s/firstItems.q%s.v%s.%s.1234567890.filter.bow.gz" % (SOURCE_DATA_DIR, QUERY_TIME, VERIFY_TIME, BASE_YEAR),"w");
for i in [1,2,3,4,5,6,7,8,9,0]:
ifs = stdOpen("%s/firstItems.q%s.v%s.%s.%d.filter.bow.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,i) );
ofs.write(ifs.read());
ifs.close();
ofs.close();
# For comment and header row of csv files, drop repeats
baseIds = [-1,-2,-3,-4,-5];
ofs = stdOpen("%s/firstItems.q%s.v%s.%s.-12345.tab.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR),"w");
isHeaderRowWritten = False;
for baseId in baseIds:
ifs = stdOpen("%s/firstItems.q%s.v%s.%s.%d.tab.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,baseId) );
for iLine, line in enumerate(ifs):
if not line.startswith(COMMENT_TAG): # Skip comment lines
if line[0].isalpha(): # Starts with a letter/label, must be header row, not data
if isHeaderRowWritten:
continue; # Skip text/header rows, except for the first one encountered
else:
isHeaderRowWritten = True;
ofs.write(line);
ifs.close();
ofs.close();
def main_prepPatientItems(argv):
prep = PreparePatientItems();
# for i in xrange(-9,10):
prep.main(["PreparePatientItems","-S",START_DATE_STR, "-E",END_DATE_STR, "-O", "-p",str(DEMOGRAPHICS_CATEGORY_ID),"-c", str(ADMIT_DX_CATEGORY_ID), "-Q", str(EVAL_TIMESPAN), "-V", str(VERIFY_TIMESPAN), "%s/better_than_expected_patients.csv" % (SOURCE_DATA_DIR), "%s/%s.tab.gz" % (SOURCE_DATA_DIR, BASE_OUTPUT_NAME)]);
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","default_recommend","source_table"];
dataModels = \
[
RowItemModel( [-1, 1, "Labs"], headers ),
RowItemModel( [-2, 1, "Imaging"], headers ),
RowItemModel( [-3, 1, "Meds"], headers ),
RowItemModel( [-4, 0, "Nursing"], headers ), # Disable default recommend to allow for checks
RowItemModel( [-5, 0, "Problems"], headers ),
RowItemModel( [-6, 1, "Lab Results"], headers ),
RowItemModel( [-7, 1, "Admit Dx"], headers ),
RowItemModel( [-8, 0, "Demographics"], 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","default_recommend","item_count","name"];
dataModels = \
[
RowItemModel( [-1, -1, 1, 30, "CBC"], headers ),
RowItemModel( [-2, -1, 1, 30, "BMP"], headers ),
RowItemModel( [-3, -1, 1, 95, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, 1, 40, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, 1, 40, "CXR"], headers ),
RowItemModel( [-6, -2, 1, 70, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, 1, 70, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, 1, 35, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, 1, 0, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, 1, 45, "Carvedilol"], headers ),
RowItemModel( [-11, -3, 1, 50, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, 1, 75, "Warfarin"], headers ),
RowItemModel( [-13, -3, 0, 0, "Ceftriaxone"], headers ), # Disable default recommend to allow for checks
RowItemModel( [-14, -4, 1, 0, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, 1, 0, "Strict I&O"], headers ),
RowItemModel( [-16, -4, 1, 0, "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( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-6, -11111, -6, datetime(2000, 2, 1, 1), 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, 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( [-11,-11, 50, 50, 50, 80, 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, 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( [ -8,-12, 15,15, 15, 15, 15, 25.0, 520.0], headers ),
RowItemModel( [ -10,-11, 12,12, 16, 16, 20, 20.0, 220.0], headers ),
RowItemModel( [ -10,-12, 10,10, 10, 10, 10, 20.0, 120.0], headers ),
];
for dataModel in dataModels:
(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_association", dataModel );
dataTextStr = \
"""item_collection_id;external_id;name;section;subgroup
-1;-1;Test Order Set - 1;Meds;TreatmentMeds
-2;-1;Test Order Set - 1;Meds;SymptomsMeds
-3;-1;Test Order Set - 1;Labs;GeneralLabs
-4;-2;Test Order Set - 2;Labs;GeneralLabs
-5;-2;Test Order Set - 2;Imaging;Xrays
-6;-2;Test Order Set - 2;Imaging;AdvancedImaging
-7;-3;Test Order Set - 3;Imaging;GeneralImaging
-8;-3;Test Order Set - 3;Nursing;GeneralNursing
-9;-3;Test Order Set - 3;Meds;RandomMeds
""" # Parse into DB insertion object
DBUtil.insertFile( StringIO(dataTextStr), "item_collection", delim=";");
dataTextStr = \
"""item_collection_item_id;item_collection_id;clinical_item_id;collection_type_id
-1;-1;-11;4
-2;-1;-12;4
-3;-1;-13;4
-4;-2;-9;4
-5;-2;-10;4
-6;-3;-1;4
-7;-3;-2;4
-8;-3;-3;4
-100;-3;-4;4
-9;-4;-1;4
-10;-4;-2;4
-11;-4;-3;4
-101;-4;-11;4
-12;-5;-5;4
-74;-6;-6;4
-77;-6;-7;4
-13;-6;-8;4
-14;-7;-5;4
-15;-7;-6;4
-16;-7;-7;4
-17;-7;-8;4
-18;-8;-14;4
-19;-8;-15;4
-20;-9;-11;4
-21;-9;-12;4
-22;-9;-13;4
""" # Parse into DB insertion object
DBUtil.insertFile( StringIO(dataTextStr), "item_collection_item", delim=";");
# Instance to test on
self.analyzer = RecommendationClassificationAnalysis();
self.preparer = PreparePatientItems();
class TestRecommendationClassificationAnalysis(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","default_recommend","source_table"];
dataModels = \
[
RowItemModel( [-1, 1, "Labs"], headers ),
RowItemModel( [-2, 1, "Imaging"], headers ),
RowItemModel( [-3, 1, "Meds"], headers ),
RowItemModel( [-4, 0, "Nursing"], headers ), # Disable default recommend to allow for checks
RowItemModel( [-5, 0, "Problems"], headers ),
RowItemModel( [-6, 1, "Lab Results"], headers ),
RowItemModel( [-7, 1, "Admit Dx"], headers ),
RowItemModel( [-8, 0, "Demographics"], 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","default_recommend","item_count","name"];
dataModels = \
[
RowItemModel( [-1, -1, 1, 30, "CBC"], headers ),
RowItemModel( [-2, -1, 1, 30, "BMP"], headers ),
RowItemModel( [-3, -1, 1, 95, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, 1, 40, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, 1, 40, "CXR"], headers ),
RowItemModel( [-6, -2, 1, 70, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, 1, 70, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, 1, 35, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, 1, 0, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, 1, 45, "Carvedilol"], headers ),
RowItemModel( [-11, -3, 1, 50, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, 1, 75, "Warfarin"], headers ),
RowItemModel( [-13, -3, 0, 0, "Ceftriaxone"], headers ), # Disable default recommend to allow for checks
RowItemModel( [-14, -4, 1, 0, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, 1, 0, "Strict I&O"], headers ),
RowItemModel( [-16, -4, 1, 0, "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( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-6, -11111, -6, datetime(2000, 2, 1, 1), 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, 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( [-11,-11, 50, 50, 50, 80, 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, 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( [ -8,-12, 15,15, 15, 15, 15, 25.0, 520.0], headers ),
RowItemModel( [ -10,-11, 12,12, 16, 16, 20, 20.0, 220.0], headers ),
RowItemModel( [ -10,-12, 10,10, 10, 10, 10, 20.0, 120.0], headers ),
];
for dataModel in dataModels:
(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_association", dataModel );
dataTextStr = \
"""item_collection_id;external_id;name;section;subgroup
-1;-1;Test Order Set - 1;Meds;TreatmentMeds
-2;-1;Test Order Set - 1;Meds;SymptomsMeds
-3;-1;Test Order Set - 1;Labs;GeneralLabs
-4;-2;Test Order Set - 2;Labs;GeneralLabs
-5;-2;Test Order Set - 2;Imaging;Xrays
-6;-2;Test Order Set - 2;Imaging;AdvancedImaging
-7;-3;Test Order Set - 3;Imaging;GeneralImaging
-8;-3;Test Order Set - 3;Nursing;GeneralNursing
-9;-3;Test Order Set - 3;Meds;RandomMeds
""" # Parse into DB insertion object
DBUtil.insertFile( StringIO(dataTextStr), "item_collection", delim=";");
dataTextStr = \
"""item_collection_item_id;item_collection_id;clinical_item_id;collection_type_id
-1;-1;-11;4
-2;-1;-12;4
-3;-1;-13;4
-4;-2;-9;4
-5;-2;-10;4
-6;-3;-1;4
-7;-3;-2;4
-8;-3;-3;4
-100;-3;-4;4
-9;-4;-1;4
-10;-4;-2;4
-11;-4;-3;4
-101;-4;-11;4
-12;-5;-5;4
-74;-6;-6;4
-77;-6;-7;4
-13;-6;-8;4
-14;-7;-5;4
-15;-7;-6;4
-16;-7;-7;4
-17;-7;-8;4
-18;-8;-14;4
-19;-8;-15;4
-20;-9;-11;4
-21;-9;-12;4
-22;-9;-13;4
""" # Parse into DB insertion object
DBUtil.insertFile( StringIO(dataTextStr), "item_collection_item", delim=";");
# Instance to test on
self.analyzer = RecommendationClassificationAnalysis();
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 patient_item_collection_link where patient_item_collection_link_id < 0");
DBUtil.execute("delete from item_collection_item where item_collection_item_id < 0");
DBUtil.execute("delete from item_collection where item_collection_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) );
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.numQueryItems = 1;
analysisQuery.numVerifyItems = 3;
analysisQuery.numRecommendations = 4;
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
analysisQuery.baseRecQuery.excludeCategoryIds = analysisQuery.recommender.defaultExcludedClinicalItemCategoryIds();
analysisQuery.baseRecQuery.excludeItemIds = analysisQuery.recommender.defaultExcludedClinicalItemIds();
#recQuery.timeDeltaMax = timedelta(0, int(self.requestData["timeDeltaMax"]) ); # Time delta to use for queries, otherwise just default to all times
colNames = ["patient_id", "TP", "FN", "FP", "recall", "precision", "F1-score", "weightRecall","weightPrecision", "normalRecall","normalPrecision", "ROC-AUC"];
# Start with default recommender
expectedResults = [ RowItemModel([-11111, 1,2,3, 0.333, 0.25, 0.286, 0.208, 0.254, 0.333/1.0, 0.25/0.75, 0.524], colNames ) ];
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line interface
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-q","1","-v","3","-r","4","-m","0","-R","BaselineFrequencyRecommender",'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","-q","1","-v","3",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","BaselineFrequencyRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Now try targeted recommender
analysisQuery.recommender = ItemAssociationRecommender();
expectedResults = [ RowItemModel([-11111, 1,2,3, 0.333, 0.25, 0.286, 0.347, 0.293, 0.333, 0.25/0.75, 0.6666], colNames ) ];
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-q","1","-v","3","-r","4","-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","-q","1","-v","3",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Now try multiple query items targeted recommender
analysisQuery.numQueryItems = 2;
expectedResults = [ RowItemModel([-11111, 1, 2, 3, 0.333, 0.25, 0.286, 0.254, 0.194, 0.333, 0.25/0.75, 0.4167], colNames ) ];
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-q","2","-v","3","-r","4","-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","-q","2","-v","3",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# More query items with aggregation options
analysisQuery.numQueryItems = 3;
expectedResults = [ RowItemModel([-11111, 1, 1, 3, 0.5, 0.25, 0.333, 0.517, 0.194, 0.5, 0.25/0.5, 0.4166], colNames ) ];
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-q","3","-v","3","-r","4","-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","-q","3","-v","3",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Value filters
analysisQuery.baseRecQuery.sortField= "freqRatio";
analysisQuery.baseRecQuery.fieldFilters["freqRatio>"] = 70;
expectedResults = [ RowItemModel([-11111, 2, 0, 2, 1.0, 0.5, 0.6666, 1.0, 0.446, 1.0, 0.5/0.5, 0.375], colNames ) ];
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
del analysisQuery.baseRecQuery.fieldFilters["freqRatio>"]; # Undo to not affect subsequent queries
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-s","freqRatio","-f","freqRatio>:70.0","-q","3","-v","3","-r","4","-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","-q","3","-v","3",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender","-s","freqRatio","-f","freqRatio>:70.0",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Unweighted aggregation
analysisQuery.baseRecQuery.weightingMethod = "unweighted";
expectedResults = [ RowItemModel([-11111, 1, 1, 3, 0.5, 0.25, 0.3333, 0.517, 0.194, 0.5, 0.25/0.5, 0.25], colNames ) ];
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-s","freqRatio","-q","3","-v","3","-r","4","-m","0","-R","ItemAssociationRecommender","-a","unweighted",'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","-q","3","-v","3",'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 = ["RecommendationClassificationAnalysis.py","-s","freqRatio","-P","-r","4","-m","0","-R","ItemAssociationRecommender","-a","unweighted",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Run by equivalent query time span selection rather than explicit counts
colNames = ["patient_id", "baseItemId", "TP", "FN", "FP", "recall", "precision", "F1-score", "weightRecall","weightPrecision", "ROC-AUC"];
expectedResults = [ RowItemModel([-11111, -4, 1, 1, 3, 0.5, 0.25, 0.333, 0.517, 0.194, 0.4167], colNames ) ];
analysisQuery.baseRecQuery.sortField= "conditionalFreq";
analysisQuery.numQueryItems = None;
analysisQuery.numVerifyItems = None;
analysisQuery.baseCategoryId = -1;
analysisQuery.queryTimeSpan = timedelta(0,3*60*60);
analysisQuery.verifyTimeSpan = timedelta(50,0);
analysisQuery.numRecommendations = 4;
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-c","-1","-Q","5400","-V","4320000","-r","4","-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","-1","-Q","5400","-V","4320000",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Run by query time span by identifying base clinical item, rather than a general category
analysisQuery.numQueryItems = None;
analysisQuery.numVerifyItems = None;
analysisQuery.baseCategoryId = None; # Clear prior setting
analysisQuery.baseItemId = -4;
analysisQuery.queryTimeSpan = timedelta(0,3*60*60);
analysisQuery.verifyTimeSpan = timedelta(50,0);
analysisQuery.numRecommendations = 4;
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-b","-4","-Q","5400","-V","4320000","-r","4","-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","-b","-4","-Q","5400","-V","4320000",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Basic then Filter test data date range
colNames = ["patient_id", "TP", "FN", "FP", "recall", "precision", "F1-score", "weightRecall","weightPrecision", "ROC-AUC"];
expectedResults = [ RowItemModel([-11111, 1, 1, 3, 0.5, 0.25, 0.33333, 0.4375, 0.29319, 0.66667], colNames ) ];
analysisQuery = AnalysisQuery();
analysisQuery.patientIds = set([-11111]);
analysisQuery.numQueryItems = 1;
analysisQuery.numVerifyItems = 2;
analysisQuery.numRecommendations = 4;
analysisQuery.recommender = ItemAssociationRecommender();
analysisQuery.baseRecQuery = RecommenderQuery();
analysisQuery.baseRecQuery.maxRecommendedId = 0; # Restrict to test data
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-q","1","-v","2","-r","4","-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","-q","1","-v","2",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
# Date Filters
colNames = ["patient_id", "TP", "FN", "FP", "recall", "precision", "F1-score", "weightRecall","weightPrecision", "ROC-AUC"];
expectedResults = [ RowItemModel([-11111, 0, 1, 2, 0.0, 0.0, 0.0, 0.0, 0.0, None], colNames ) ];
analysisQuery = AnalysisQuery();
analysisQuery.patientIds = set([-11111]);
analysisQuery.numQueryItems = 1;
analysisQuery.numVerifyItems = 2;
analysisQuery.numRecommendations = 4;
analysisQuery.recommender = ItemAssociationRecommender();
analysisQuery.baseRecQuery = RecommenderQuery();
analysisQuery.baseRecQuery.maxRecommendedId = 0; # Restrict to test data
analysisQuery.startDate = datetime(2000,1,1,1);
analysisQuery.endDate = datetime(2000,1,10);
analysisResults = self.analyzer(analysisQuery);
self.assertEqualStatResults(expectedResults, analysisResults, colNames);
# Redo with command-line
sys.stdout = StringIO(); # Redirect stdout output to collect test results
argv = ["RecommendationClassificationAnalysis.py","-q","1","-v","2","-r","4","-m","0","-S","2000-01-01 01:00:00","-E","2000-01-10","-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","-q","1","-v","2","-S","2000-01-01 01:00:00","-E","2000-01-10",'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 = ["RecommendationClassificationAnalysis.py","-P","-r","4","-m","0","-R","ItemAssociationRecommender",'-',"-"];
self.analyzer.main(argv);
textOutput = StringIO(sys.stdout.getvalue());
self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
def test_numRecsByOrderSet(self):
# Designate number of recommendations indirectly via linked order set id
DBUtil.execute("update clinical_item set default_recommend = 0 where clinical_item_id = -8"); # Disable default recommend on one item to shift results
colNames = ["patient_id", "TP", "FN", "FP", "recall", "precision", "F1-score", "weightRecall","weightPrecision", "ROC-AUC"];
expectedResults = [ RowItemModel([-11111, 2, 0, 3, 1.0, 0.4, 0.571, 1.0, 0.3178, 0.4167], colNames ) ];
# Do through fabricated prepared file intermediary
sys.stdout = StringIO();
argv = ["PreparePatientItems.py","-q","2","-v","3",'0,-11111',"-"];
self.preparer.main(argv);
preparedDataFile = StringIO(sys.stdout.getvalue());
# Artificially add a key order set ID for the fabricated data
modFile = StringIO();
formatter = TextResultsFormatter(modFile);
dataCols = None;
for i, dataRow in enumerate(TabDictReader(preparedDataFile)):
dataRow["order_set_id"] = TEST_ORDERSET_ID;
if i <= 0:
dataCols = list(dataRow.keys());
formatter.formatTuple(dataCols); # Insert a mock record to get a header / label row
formatter.formatResultDict(dataRow, dataCols);
preparedDataFile = StringIO(modFile.getvalue());
sys.stdin = preparedDataFile; # Read prepared data file from redirected stdin
sys.stdout = StringIO();
#argv = ["RecommendationClassificationAnalysis.py","-P","-r","5","-m","0","-R","ItemAssociationRecommender",'-',"-"];
argv = ["RecommendationClassificationAnalysis.py","-P","--numRecsByOrderSet","-m","0","-R","ItemAssociationRecommender",'-',"-"];
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 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.purgeTestRecords();
self.clinicalItemCategoryIdStrList = list()
headers = [
"clinical_item_category_id", "default_recommend", "source_table"
]
dataModels = \
[
RowItemModel( [-1, 1, "Labs"], headers ),
RowItemModel( [-2, 1, "Imaging"], headers ),
RowItemModel( [-3, 1, "Meds"], headers ),
RowItemModel( [-4, 1, "Nursing"], headers ),
RowItemModel( [-5, 0, "Problems"], headers ),
RowItemModel( [-6, 1, "Lab Results"], headers ),
RowItemModel( [-7, 1, "Admit Dx"], headers ),
RowItemModel( [-8, 0, "Demographics"], 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",
"default_recommend", "name"
]
dataModels = \
[
RowItemModel( [-1, -1, 1, 1, "CBC"], headers ),
RowItemModel( [-2, -1, 1, 1, "BMP"], headers ),
RowItemModel( [-3, -1, 1, 1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, 1, 1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, 1, 1, "CXR"], headers ),
RowItemModel( [-6, -2, 1, 1, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, 1, 1, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, 1, 1, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, 1, 1, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, 1, 1, "Carvedilol"], headers ),
RowItemModel( [-11, -3, 1, 1, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, 1, 1, "Warfarin"], headers ),
RowItemModel( [-13, -3, 1, 0, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, 1, 1, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, 1, 1, "Strict I&O"], headers ),
RowItemModel( [-16, -4, 1, 1, "Fall Precautions"], headers ),
RowItemModel( [-22, -5, 1, 1, "Diagnosis 2"], headers ),
RowItemModel( [-23, -5, 1, 1, "Diagnosis 3"], headers ),
RowItemModel( [-24, -5, 1, 1, "Diagnosis 4"], headers ),
RowItemModel( [-21, -7, 0, 1, "Diagnosis 1 (Admit)"], headers ),
RowItemModel( [-25, -7, 1, 1, "Diagnosis 2 (Admit)"], headers ),
RowItemModel( [-30, -6, 1, 1, "Troponin (High)"], headers ),
RowItemModel( [-31, -6, 1, 1, "BNP (High)"], headers ),
RowItemModel( [-32, -6, 1, 1, "Creatinine (High)"], headers ),
RowItemModel( [-33, -6, 1, 1, "ESR (High)"], headers ), # Default exclude from recommendations
RowItemModel( [-41, -8, 1, 1, "Male"], headers ),
RowItemModel( [-42, -8, 1, 1, "Female"], headers ),
RowItemModel( [-43, -8, 1, 1, "Birth"], headers ),
RowItemModel( [-44, -8, 1, 1, "Birth1980s"], headers ),
RowItemModel( [-45, -8, 1, 1, "Birth1970s"], headers ),
RowItemModel( [-46, -8, 1, 1, "RaceWhite"], headers ),
RowItemModel( [-47, -8, 1, 1, "RaceBlack"], headers ),
RowItemModel( [-49, -8, 1, 1, "Death"], 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( [-101,-11111, -43, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-102,-11111, -45, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-103,-11111, -41, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-104,-11111, -46, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
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( [-4, -11111, -4, datetime(2000, 1, 1, 3), datetime(2010, 1, 1, 0)], headers ), # Repeat item
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( [-63, -11111, -13, datetime(2000, 1, 4, 5), datetime(2010, 1, 1, 0)], headers ), # Exclude item
RowItemModel( [-64, -11111, -24, datetime(2000, 1, 4,10), datetime(2010, 1, 1, 0)], headers ), # Exclude category
RowItemModel( [-62, -11111, -31, datetime(2000, 1,10, 0), datetime(2010, 1, 1, 0)], headers ), # Beyond 1 week
RowItemModel( [-71, -11111, -8, datetime(2000, 1, 4, 1), datetime(2010, 1, 1, 0)], headers ), # Repeat query item within 1 week verify period, don't use as a verify item
RowItemModel( [-201, -11111,-49, datetime(2009, 1, 1, 1), datetime(2010, 1, 1, 0)], headers ), # Death date in far future
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-121,-22222, -43, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-122,-22222, -44, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-123,-22222, -42, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-124,-22222, -47, datetime(1983, 5, 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( [-131,-33333, -43, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-132,-33333, -44, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-133,-33333, -42, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-134,-33333, -46, datetime(1983, 5, 1, 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 ),
RowItemModel( [-141,-44444, -43, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-142,-44444, -45, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-143,-44444, -42, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-144,-44444, -46, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-20, -44444, -21, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ), # Admit diagnosis
RowItemModel( [-22, -44444, -6, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ), # Items recorded with date level precision, not time
RowItemModel( [-23, -44444, -12, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-24, -44444, -11, datetime(2000, 1, 6, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-25, -44444, -8, datetime(2000, 1, 6, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-204,-44444,-49, datetime(2000, 2, 1, 1), datetime(2010, 1, 1, 0)], headers ), # Death date within 1 month
# Order Set Usage example
RowItemModel( [-5002,-55555, -3, datetime(2000,10, 1, 0, 0), datetime(2010, 1, 1, 0)], headers ), # Very old item, not relevant to current query
RowItemModel( [-5040,-55555, -25, datetime(2000,10,10, 0, 0), datetime(2010, 1, 1, 0)], headers ), # Admit diagnosis (coded at date level precision before time-level order data)
RowItemModel( [-5005,-55555, -2, datetime(2000,10,10,10, 0), datetime(2010, 1, 1, 0)], headers ), # Non-order set item before
RowItemModel( [-5010,-55555, -1, datetime(2000,10,10,10, 5), datetime(2010, 1, 1, 0)], headers ), # Order Set 1
RowItemModel( [-5020,-55555, -9, datetime(2000,10,10,10, 5), datetime(2010, 1, 1, 0)], headers ), # Order Set 1
RowItemModel( [-5030,-55555, -5, datetime(2000,10,10,10,10), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc within 1 hour
RowItemModel( [-5050,-55555, -8, datetime(2000,10,10,10,30), datetime(2010, 1, 1, 0)], headers ), # Order Set 2
RowItemModel( [-5060,-55555, -11, datetime(2000,10,10,10,30), datetime(2010, 1, 1, 0)], headers ), # Order Set 2
RowItemModel( [-5070,-55555, -12, datetime(2000,10,10,10,30), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc Within 1 hour
RowItemModel( [-5080,-55555, -10, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc 10 hours later
RowItemModel( [-5090,-55555, -1, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Order Set 1 again (ignore repeats)
RowItemModel( [-5100,-55555, -2, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Order Set 1 again
RowItemModel( [-5110,-55555, -3, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc 10 hours later
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
dataTextStr = \
"""item_collection_id;external_id;name;section;subgroup
-1;-1;Test Order Set - 1;Meds;TreatmentMeds
-2;-1;Test Order Set - 1;Meds;SymptomsMeds
-3;-1;Test Order Set - 1;Labs;GeneralLabs
-4;-2;Test Order Set - 2;Labs;GeneralLabs
-5;-2;Test Order Set - 2;Imaging;Xrays
-6;-2;Test Order Set - 2;Imaging;AdvancedImaging
-7;-3;Test Order Set - 3;Imaging;GeneralImaging
-8;-3;Test Order Set - 3;Nursing;GeneralNursing
""" # Parse into DB insertion object
DBUtil.insertFile(StringIO(dataTextStr), "item_collection", delim=";")
dataTextStr = \
"""item_collection_item_id;item_collection_id;clinical_item_id;collection_type_id
-1;-1;-11;4
-2;-1;-12;4
-3;-1;-13;4
-4;-2;-9;4
-5;-2;-10;4
-6;-3;-1;4
-7;-3;-2;4
-8;-3;-3;4
-100;-3;-4;4
-9;-4;-1;4
-10;-4;-2;4
-11;-4;-3;4
-101;-4;-11;4
-12;-5;-5;4
-74;-6;-6;4
-77;-6;-7;4
-13;-6;-8;4
-14;-7;-5;4
-15;-7;-6;4
-16;-7;-7;4
-17;-7;-8;4
-18;-8;-14;4
-19;-8;-15;4
""" # Parse into DB insertion object
DBUtil.insertFile(StringIO(dataTextStr),
"item_collection_item",
delim=";")
dataTextStr = \
"""patient_item_collection_link_id;patient_item_id;item_collection_item_id
-1;-5010;-6
-2;-5020;-4
-3;-5050;-13
-4;-5060;-101
-5;-5090;-6
-6;-5100;-7
""" # Parse into DB insertion object
DBUtil.insertFile(StringIO(dataTextStr),
"patient_item_collection_link",
delim=";")
# Instance to test on
self.analyzer = PreparePatientItems()
class TestPreparePatientItems(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.purgeTestRecords();
self.clinicalItemCategoryIdStrList = list()
headers = [
"clinical_item_category_id", "default_recommend", "source_table"
]
dataModels = \
[
RowItemModel( [-1, 1, "Labs"], headers ),
RowItemModel( [-2, 1, "Imaging"], headers ),
RowItemModel( [-3, 1, "Meds"], headers ),
RowItemModel( [-4, 1, "Nursing"], headers ),
RowItemModel( [-5, 0, "Problems"], headers ),
RowItemModel( [-6, 1, "Lab Results"], headers ),
RowItemModel( [-7, 1, "Admit Dx"], headers ),
RowItemModel( [-8, 0, "Demographics"], 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",
"default_recommend", "name"
]
dataModels = \
[
RowItemModel( [-1, -1, 1, 1, "CBC"], headers ),
RowItemModel( [-2, -1, 1, 1, "BMP"], headers ),
RowItemModel( [-3, -1, 1, 1, "Hepatic Panel"], headers ),
RowItemModel( [-4, -1, 1, 1, "Cardiac Enzymes"], headers ),
RowItemModel( [-5, -2, 1, 1, "CXR"], headers ),
RowItemModel( [-6, -2, 1, 1, "RUQ Ultrasound"], headers ),
RowItemModel( [-7, -2, 1, 1, "CT Abdomen/Pelvis"], headers ),
RowItemModel( [-8, -2, 1, 1, "CT PE Thorax"], headers ),
RowItemModel( [-9, -3, 1, 1, "Acetaminophen"], headers ),
RowItemModel( [-10, -3, 1, 1, "Carvedilol"], headers ),
RowItemModel( [-11, -3, 1, 1, "Enoxaparin"], headers ),
RowItemModel( [-12, -3, 1, 1, "Warfarin"], headers ),
RowItemModel( [-13, -3, 1, 0, "Ceftriaxone"], headers ),
RowItemModel( [-14, -4, 1, 1, "Foley Catheter"], headers ),
RowItemModel( [-15, -4, 1, 1, "Strict I&O"], headers ),
RowItemModel( [-16, -4, 1, 1, "Fall Precautions"], headers ),
RowItemModel( [-22, -5, 1, 1, "Diagnosis 2"], headers ),
RowItemModel( [-23, -5, 1, 1, "Diagnosis 3"], headers ),
RowItemModel( [-24, -5, 1, 1, "Diagnosis 4"], headers ),
RowItemModel( [-21, -7, 0, 1, "Diagnosis 1 (Admit)"], headers ),
RowItemModel( [-25, -7, 1, 1, "Diagnosis 2 (Admit)"], headers ),
RowItemModel( [-30, -6, 1, 1, "Troponin (High)"], headers ),
RowItemModel( [-31, -6, 1, 1, "BNP (High)"], headers ),
RowItemModel( [-32, -6, 1, 1, "Creatinine (High)"], headers ),
RowItemModel( [-33, -6, 1, 1, "ESR (High)"], headers ), # Default exclude from recommendations
RowItemModel( [-41, -8, 1, 1, "Male"], headers ),
RowItemModel( [-42, -8, 1, 1, "Female"], headers ),
RowItemModel( [-43, -8, 1, 1, "Birth"], headers ),
RowItemModel( [-44, -8, 1, 1, "Birth1980s"], headers ),
RowItemModel( [-45, -8, 1, 1, "Birth1970s"], headers ),
RowItemModel( [-46, -8, 1, 1, "RaceWhite"], headers ),
RowItemModel( [-47, -8, 1, 1, "RaceBlack"], headers ),
RowItemModel( [-49, -8, 1, 1, "Death"], 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( [-101,-11111, -43, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-102,-11111, -45, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-103,-11111, -41, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-104,-11111, -46, datetime(1972, 1, 1, 0), datetime(2010, 1, 1, 0)], headers ),
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( [-4, -11111, -4, datetime(2000, 1, 1, 3), datetime(2010, 1, 1, 0)], headers ), # Repeat item
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( [-63, -11111, -13, datetime(2000, 1, 4, 5), datetime(2010, 1, 1, 0)], headers ), # Exclude item
RowItemModel( [-64, -11111, -24, datetime(2000, 1, 4,10), datetime(2010, 1, 1, 0)], headers ), # Exclude category
RowItemModel( [-62, -11111, -31, datetime(2000, 1,10, 0), datetime(2010, 1, 1, 0)], headers ), # Beyond 1 week
RowItemModel( [-71, -11111, -8, datetime(2000, 1, 4, 1), datetime(2010, 1, 1, 0)], headers ), # Repeat query item within 1 week verify period, don't use as a verify item
RowItemModel( [-201, -11111,-49, datetime(2009, 1, 1, 1), datetime(2010, 1, 1, 0)], headers ), # Death date in far future
RowItemModel( [-5, -11111, -12, datetime(2000, 2, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-121,-22222, -43, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-122,-22222, -44, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-123,-22222, -42, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-124,-22222, -47, datetime(1983, 5, 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( [-131,-33333, -43, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-132,-33333, -44, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-133,-33333, -42, datetime(1983, 5, 1, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-134,-33333, -46, datetime(1983, 5, 1, 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 ),
RowItemModel( [-141,-44444, -43, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-142,-44444, -45, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-143,-44444, -42, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-144,-44444, -46, datetime(1975, 3, 3, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-20, -44444, -21, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ), # Admit diagnosis
RowItemModel( [-22, -44444, -6, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ), # Items recorded with date level precision, not time
RowItemModel( [-23, -44444, -12, datetime(2000, 1, 5, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-24, -44444, -11, datetime(2000, 1, 6, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-25, -44444, -8, datetime(2000, 1, 6, 0), datetime(2010, 1, 1, 0)], headers ),
RowItemModel( [-204,-44444,-49, datetime(2000, 2, 1, 1), datetime(2010, 1, 1, 0)], headers ), # Death date within 1 month
# Order Set Usage example
RowItemModel( [-5002,-55555, -3, datetime(2000,10, 1, 0, 0), datetime(2010, 1, 1, 0)], headers ), # Very old item, not relevant to current query
RowItemModel( [-5040,-55555, -25, datetime(2000,10,10, 0, 0), datetime(2010, 1, 1, 0)], headers ), # Admit diagnosis (coded at date level precision before time-level order data)
RowItemModel( [-5005,-55555, -2, datetime(2000,10,10,10, 0), datetime(2010, 1, 1, 0)], headers ), # Non-order set item before
RowItemModel( [-5010,-55555, -1, datetime(2000,10,10,10, 5), datetime(2010, 1, 1, 0)], headers ), # Order Set 1
RowItemModel( [-5020,-55555, -9, datetime(2000,10,10,10, 5), datetime(2010, 1, 1, 0)], headers ), # Order Set 1
RowItemModel( [-5030,-55555, -5, datetime(2000,10,10,10,10), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc within 1 hour
RowItemModel( [-5050,-55555, -8, datetime(2000,10,10,10,30), datetime(2010, 1, 1, 0)], headers ), # Order Set 2
RowItemModel( [-5060,-55555, -11, datetime(2000,10,10,10,30), datetime(2010, 1, 1, 0)], headers ), # Order Set 2
RowItemModel( [-5070,-55555, -12, datetime(2000,10,10,10,30), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc Within 1 hour
RowItemModel( [-5080,-55555, -10, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc 10 hours later
RowItemModel( [-5090,-55555, -1, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Order Set 1 again (ignore repeats)
RowItemModel( [-5100,-55555, -2, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Order Set 1 again
RowItemModel( [-5110,-55555, -3, datetime(2000,10,10,20, 0), datetime(2010, 1, 1, 0)], headers ), # Ad-hoc 10 hours later
]
for dataModel in dataModels:
(dataItemId,
isNew) = DBUtil.findOrInsertItem("patient_item", dataModel)
dataTextStr = \
"""item_collection_id;external_id;name;section;subgroup
-1;-1;Test Order Set - 1;Meds;TreatmentMeds
-2;-1;Test Order Set - 1;Meds;SymptomsMeds
-3;-1;Test Order Set - 1;Labs;GeneralLabs
-4;-2;Test Order Set - 2;Labs;GeneralLabs
-5;-2;Test Order Set - 2;Imaging;Xrays
-6;-2;Test Order Set - 2;Imaging;AdvancedImaging
-7;-3;Test Order Set - 3;Imaging;GeneralImaging
-8;-3;Test Order Set - 3;Nursing;GeneralNursing
""" # Parse into DB insertion object
DBUtil.insertFile(StringIO(dataTextStr), "item_collection", delim=";")
dataTextStr = \
"""item_collection_item_id;item_collection_id;clinical_item_id;collection_type_id
-1;-1;-11;4
-2;-1;-12;4
-3;-1;-13;4
-4;-2;-9;4
-5;-2;-10;4
-6;-3;-1;4
-7;-3;-2;4
-8;-3;-3;4
-100;-3;-4;4
-9;-4;-1;4
-10;-4;-2;4
-11;-4;-3;4
-101;-4;-11;4
-12;-5;-5;4
-74;-6;-6;4
-77;-6;-7;4
-13;-6;-8;4
-14;-7;-5;4
-15;-7;-6;4
-16;-7;-7;4
-17;-7;-8;4
-18;-8;-14;4
-19;-8;-15;4
""" # Parse into DB insertion object
DBUtil.insertFile(StringIO(dataTextStr),
"item_collection_item",
delim=";")
dataTextStr = \
"""patient_item_collection_link_id;patient_item_id;item_collection_item_id
-1;-5010;-6
-2;-5020;-4
-3;-5050;-13
-4;-5060;-101
-5;-5090;-6
-6;-5100;-7
""" # Parse into DB insertion object
DBUtil.insertFile(StringIO(dataTextStr),
"patient_item_collection_link",
delim=";")
# Instance to test on
self.analyzer = PreparePatientItems()
def tearDown(self):
"""Restore state from any setUp or test steps"""
self.purgeTestRecords()
DBTestCase.tearDown(self)
def purgeTestRecords(self):
log.info("Purge test records from the database")
DBUtil.execute(
"delete from patient_item_collection_link where patient_item_collection_link_id < 0"
)
DBUtil.execute(
"delete from item_collection_item where item_collection_item_id < 0"
)
DBUtil.execute(
"delete from item_collection where item_collection_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 < 0"
)
def test_analysisPreparation(self):
# Run the analysis preparer against the mock test data above and verify expected data afterwards.
analysisQuery = AnalysisQuery()
analysisQuery.patientIds = set([-11111])
analysisQuery.baseCategoryId = -7
analysisQuery.queryTimeSpan = timedelta(0, 86400)
analysisQuery.verifyTimeSpan = timedelta(0, 604800)
analysisQuery.baseRecQuery = RecommenderQuery()
analysisQuery.baseRecQuery.targetItemIds = set([-33, -32, -31, -30])
analysisQuery.baseRecQuery.excludeItemIds = [-13]
analysisQuery.baseRecQuery.excludeCategoryIds = [-5]
analysisQuery.baseRecQuery.maxRecommendedId = 0
# Restrict to test data
# Initial run without time limits on outcome measure
colNames = [
"patient_id", "baseItemId", "queryItemCountById",
"verifyItemCountById", "outcome.-33", "outcome.-32", "outcome.-31",
"outcome.-30"
]
expectedResults = [
RowItemModel([
-11111, -21, {
-4: 2,
-10: 1,
-8: 1,
-32: 1
}, {
-30: 1
}, +0, +2, +1, +1
], colNames)
]
analysisResults = list(self.analyzer(analysisQuery))
self.assertEqualResultDicts(expectedResults, analysisResults, colNames)
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"PreparePatientItems.py", "-c", "-7", "-Q", "86400", "-V",
"604800", "-o", "-33,-32,-31,-30", '0,-11111', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualTextOutput(expectedResults, textOutput, colNames)
# Now try with time limitation on outcome measure
analysisQuery.baseRecQuery.timeDeltaMax = timedelta(0, 604800)
# 1 week
colNames = [
"patient_id", "queryItemCountById", "verifyItemCountById",
"outcome.-33", "outcome.-32", "outcome.-30"
]
expectedResults = [
RowItemModel(
[-11111, {
-4: 2,
-10: 1,
-8: 1,
-32: 1
}, {
-30: 1
}, +0, +2, +1], colNames)
]
analysisResults = list(self.analyzer(analysisQuery))
self.assertEqualResultDicts(expectedResults, analysisResults, colNames)
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"PreparePatientItems.py", "-c", "-7", "-Q", "86400", "-V",
"604800", "-t", "604800", "-o", "-33,-32,-31,-30", '0,-11111', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualTextOutput(expectedResults, textOutput, colNames)
# Now include all historical (demographic) data items
analysisQuery.pastCategoryIds = [-8]
colNames = [
"patient_id", "queryItemCountById", "verifyItemCountById",
"outcome.-33", "outcome.-32", "outcome.-30"
]
expectedResults = [
RowItemModel([
-11111, {
-43: 1,
-45: 1,
-41: 1,
-46: 1,
-4: 2,
-10: 1,
-8: 1,
-32: 1
}, {
-30: 1
}, +0, +2, +1
], colNames)
]
analysisResults = list(self.analyzer(analysisQuery))
self.assertEqualResultDicts(expectedResults, analysisResults, colNames)
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"PreparePatientItems.py", "-p", "-8", "-c", "-7", "-Q", "86400",
"-V", "604800", "-t", "604800", "-o", "-33,-32,-31,-30",
'0,-11111', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualTextOutput(expectedResults, textOutput, colNames)
# Different search where items are recorded with date level instead of time level precision
# Note that use a verify time threshold of (1 day + 1 second) so can just capture the next day of of data instead of just missing it
colNames = [
"patient_id", "baseItemId", "queryItemCountById",
"verifyItemCountById", "outcome.-33", "outcome.-32", "outcome.-31",
"outcome.-30"
]
expectedResults = [
RowItemModel([
-44444, -21, {
-6: 1,
-12: 1
}, {
-11: 1,
-8: 1
}, +0, +0, +0, +0
], colNames)
]
# Run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"PreparePatientItems.py", "-c", "-7", "-Q", "14400", "-V", "86401",
"-o", "-33,-32,-31,-30", '0,-44444', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualTextOutput(expectedResults, textOutput, colNames)
# Include background demographics category
colNames = [
"patient_id", "baseItemId", "queryItemCountById",
"verifyItemCountById", "outcome.-33", "outcome.-32", "outcome.-31",
"outcome.-30"
]
expectedResults = [
RowItemModel([
-44444, -21, {
-43: 1,
-45: 1,
-42: 1,
-46: 1,
-6: 1,
-12: 1
}, {
-11: 1,
-8: 1
}, +0, +0, +0, +0
], colNames)
]
# Run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"PreparePatientItems.py", "-p", "-8", "-c", "-7", "-Q", "14400",
"-V", "86401", "-o", "-33,-32,-31,-30", '0,-44444', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
self.assertEqualTextOutput(expectedResults, textOutput, colNames)
def test_orderSetAnalysisPreparer(self):
# Run the analysis preparer against the mock test data above and verify expected data afterwards.
# Use existing order set usage as reference points
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"PreparePatientItems.py", "-O", "-c", "-7", "-Q", "86400", "-V",
"3600", '0,-55555,-11111', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
colNames = [
"patient_id", "baseItemId", "queryItemCountById",
"verifyItemCountById", "order_set_id"
]
expectedResults = \
[ RowItemModel([-55555, -25, {-25:1,-2:1}, {-1:1,-9:1,-5:1,-8:1,-11:1,-12:1}, -1], colNames ), # Diagnosis not in verify items
RowItemModel([-55555, -25, {-25:1,-2:1,-1:1,-9:1,-5:1}, {-8:1,-11:1,-12:1}, -2], colNames ) # Diagnosis okay to be in query items
]
self.assertEqualTextOutput(expectedResults, textOutput, colNames)
def assertEqualResultDicts(self, expectedResults, analysisResults,
colNames):
self.assertEquals(len(expectedResults), len(analysisResults))
for expectedResult, analysisResult in zip(expectedResults,
analysisResults):
self.assertEqualDict(expectedResult, analysisResult, colNames)
def assertEqualTextOutput(self, expectedResults, textOutput, colNames):
"""Convenience function to verify text output from a program to match the provided symbolic expected results,
by parsing the text into structured fields."""
headerLine = None
while headerLine is None:
nextLine = textOutput.readline()
if not nextLine.startswith(COMMENT_TAG):
headerLine = nextLine
headers = headerLine.strip().split("\t")
analysisResults = list()
for line in textOutput:
dataChunks = line.strip().split("\t")
resultModel = RowItemModel(dataChunks, headers)
# Convert the target elements of interest into numerical values
for col in colNames:
if col not in resultModel:
# Look for a JSON encoded version
jsonCol = "%sJSON" % col
if jsonCol in resultModel:
resultModel[col] = loadJSONDict(
resultModel[jsonCol], int, int)
else:
resultModel[col] = int(resultModel[col])
analysisResults.append(resultModel)
#for col in colNames:
# print >> sys.stderr, col, expectedResults[0][col], analysisResults[0][col]
self.assertEqualStatResults(expectedResults, analysisResults, colNames)
def test_parsePreparedResultFile(self):
# Run the analysis preparer against the mock test data above and verify can parse back text file into original object form.
# Key columns to verify
colNames = \
[ "patient_id",
"baseItemId",
"baseItemDate",
"queryStartTime",
"queryEndTime",
"verifyEndTime",
"queryItemCountById",
"verifyItemCountById",
"outcome.-33","outcome.-32", "outcome.-31","outcome.-30"
]
# Initial run without time limits on outcome measure
analysisQuery = AnalysisQuery()
analysisQuery.patientIds = set([-11111, -44444])
analysisQuery.baseCategoryId = -7
analysisQuery.queryTimeSpan = timedelta(0, 86400)
analysisQuery.verifyTimeSpan = timedelta(0, 604800)
analysisQuery.baseRecQuery = RecommenderQuery()
analysisQuery.baseRecQuery.targetItemIds = set([-33, -32, -31, -30])
analysisQuery.baseRecQuery.excludeItemIds = [-13]
analysisQuery.baseRecQuery.excludeCategoryIds = [-5]
analysisQuery.baseRecQuery.maxRecommendedId = 0
# Restrict to test data
directResults = list(self.analyzer(analysisQuery))
# Redo but run through command-line interface
sys.stdout = StringIO()
# Redirect stdout output to collect test results
argv = [
"PreparePatientItems.py", "-c", "-7", "-Q", "86400", "-V",
"604800", "-o", "-33,-32,-31,-30", '0,-11111,-44444', "-"
]
self.analyzer.main(argv)
textOutput = StringIO(sys.stdout.getvalue())
textBasedResults = list(
self.analyzer.parsePreparedResultFile(textOutput))
self.assertEqualResultDicts(directResults, textBasedResults, colNames)
def test_convertResultsFileToFeatureMatrix(self):
inputFile = StringIO ( \
"""# {"argv": ["medinfo\\cpoe\\analysis\\PreparePatientItems.py", "-c", "2", "-Q", "14400", "-V", "86401", "-o", "27427", "-t", "2592000", "temp\\patientIds.test.tab", "test.out"]}
patient_id\tbaseItemId\tbaseItemDate\tqueryStartTime\tqueryEndTime\tverifyEndTime\tqueryItemCountByIdJSON\tverifyItemCountByIdJSON\toutcome.-1
50559\t-21\t2010-02-04 00:00:00\t2010-02-04 00:00:00\t2010-02-04 04:00:00\t2010-02-05 00:00:01\t{"20449":1, "132":2, "133":3}\t{"19596":1, "19694":1}\t0
52137\t-21\t2013-03-18 00:00:00\t2013-03-18 00:00:00\t2013-03-18 04:00:00\t2013-03-19 00:00:01\t{"20388":1, "19766":10}\t{"19622":4, "19766":7}\t1
35141\t-21\t2012-07-17 00:00:00\t2012-07-17 00:00:00\t2012-07-17 04:00:00\t2012-07-18 00:00:01\t{"13326":1, "5778":1, "13589":1}\t{"19810":1, "19724":1, "13474":1}\t0
19347\t-21\t2010-11-26 00:00:00\t2010-11-26 00:00:00\t2010-11-26 04:00:00\t2010-11-27 00:00:01\t{"13312":1, "19840":1, "13318":1}\t{}\t0
""")
expectedResults = \
[ ["patient_id","baseItemId","baseItemDate","queryStartTime","queryEndTime","verifyEndTime", "outcome.-1", "132","133","5778","13312","13318","13326","13474","13589","19596","19622","19694","19724","19766","19810","19840","20388","20449"],
["50559","-21","2010-02-04 00:00:00","2010-02-04 00:00:00","2010-02-04 04:00:00","2010-02-05 00:00:01","0", 2, 3, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1],
["52137","-21","2013-03-18 00:00:00","2013-03-18 00:00:00","2013-03-18 04:00:00","2013-03-19 00:00:01","1", 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 17, 0, 0, 1, 0],
["35141","-21","2012-07-17 00:00:00","2012-07-17 00:00:00","2012-07-17 04:00:00","2012-07-18 00:00:01","0", 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0],
["19347","-21","2010-11-26 00:00:00","2010-11-26 00:00:00","2010-11-26 04:00:00","2010-11-27 00:00:01","0", 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
]
results = list(
self.analyzer.convertResultsFileToFeatureMatrix(inputFile,
incHeaders=True))
self.assertEqualList(expectedResults, results)
def test_convertResultsFileToBagOfWordsCorpus(self):
inputFileStr = \
"""# {"argv": ["medinfo\\cpoe\\analysis\\PreparePatientItems.py", "-c", "2", "-Q", "14400", "-V", "86401", "-o", "27427", "-t", "2592000", "temp\\patientIds.test.tab", "test.out"]}
patient_id\tbaseItemId\tbaseItemDate\tqueryStartTime\tqueryEndTime\tverifyEndTime\tqueryItemCountByIdJSON\tverifyItemCountByIdJSON\toutcome.-1
50559\t-21\t2010-02-04 00:00:00\t2010-02-04 00:00:00\t2010-02-04 04:00:00\t2010-02-05 00:00:01\t{"20449":1, "132":2, "133":3}\t{"19596":1, "19694":1}\t0
52137\t-21\t2013-03-18 00:00:00\t2013-03-18 00:00:00\t2013-03-18 04:00:00\t2013-03-19 00:00:01\t{"20388":1, "19766":10}\t{"19622":4, "19766":7}\t1
35141\t-21\t2012-07-17 00:00:00\t2012-07-17 00:00:00\t2012-07-17 04:00:00\t2012-07-18 00:00:01\t{"13326":1, "5778":1, "13589":1}\t{"19810":1, "19724":1, "13474":1}\t0
19347\t-21\t2010-11-26 00:00:00\t2010-11-26 00:00:00\t2010-11-26 04:00:00\t2010-11-27 00:00:01\t{"13312":1, "19840":1, "13318":1}\t{}\t0
"""
expectedResults = \
[ [(-1,0),(20449,1),(132,2),(133,3),(19596,1),(19694,1)],
[(-1,1),(20388,1),(19766,17),(19622,4)],
[(-1,0),(13326,1),(5778,1),(13589,1),(19810,1),(19724,1),(13474,1)],
[(-1,0),(13312,1),(19840,1),(13318,1)],
]
results = list(
self.analyzer.convertResultsFileToBagOfWordsCorpus(
StringIO(inputFileStr),
queryItems=True,
verifyItems=True,
outcomeItems=True))
self.assertEqualBagOfWordsList(expectedResults, results)
expectedResults = \
[ [(20449,1),(132,2),(133,3)],
[(20388,1),(19766,10)],
[(13326,1),(5778,1),(13589,1)],
[(13312,1),(19840,1),(13318,1)],
]
results = list(
self.analyzer.convertResultsFileToBagOfWordsCorpus(
StringIO(inputFileStr),
queryItems=True,
verifyItems=False,
outcomeItems=False))
self.assertEqualBagOfWordsList(expectedResults, results)
expectedResults = \
[ [(19596,1),(19694,1)],
[(19622,4),(19766,7)],
[(19810,1),(19724,1),(13474,1)],
[],
]
results = list(
self.analyzer.convertResultsFileToBagOfWordsCorpus(
StringIO(inputFileStr),
queryItems=False,
verifyItems=True,
outcomeItems=False))
self.assertEqualBagOfWordsList(expectedResults, results)
def assertEqualBagOfWordsList(self, expectedResults, results):
"""Convert random order Bag of Words items into consistent dictionaries for comparison"""
for i, result in enumerate(expectedResults):
expectedResults[i] = self.analyzer.bagOfWordsToCountById(result)
for i, result in enumerate(results):
results[i] = self.analyzer.bagOfWordsToCountById(result)
self.assertEqualList(expectedResults, results)