def _getAdmitDxPatientFrequencyRankByYear(self):
# Get list of all clinical item IDs matching admit diagnosis.
# Get this list in advance to make subsequent query run a bit faster.
admitDxClinicalItemIds = self._getAdmitDxClinicalItemIds()
# Build query for # of unique patients.
# SELECT
# ci.name AS icd_code,
# ci.description AS admit_dx,
# EXTRACT(YEAR FROM pi.item_date) AS admit_year,
# COUNT(DISTINCT pi.patient_id) AS num_unique_patients,
# FROM
# patient_item AS pi
# JOIN
# clinical_item AS ci
# ON
# pi.clinical_item_id = ci.clinical_item_id
# WHERE
# ci.clinical_item_id in (admitDxClinicalItemIds)
# GROUP BY
# icd_code,
# admit_dx,
# admit_year
# num_unique_patients
# ORDER BY
# admit_year,
# num_unique_patients DESC
query = SQLQuery()
query.addSelect("ci.name AS icd_code")
query.addSelect("ci.description AS admit_dx")
query.addSelect("EXTRACT(YEAR FROM pi.item_date) AS admit_year")
query.addSelect("COUNT(DISTINCT pi.patient_id) AS num_unique_patients")
query.addFrom("patient_item AS pi")
query.addJoin("clinical_item AS ci",
"pi.clinical_item_id = ci.clinical_item_id")
query.addWhereIn("ci.clinical_item_id", admitDxClinicalItemIds)
query.addGroupBy("icd_code")
query.addGroupBy("admit_dx")
query.addGroupBy("admit_year")
query.addGroupBy("num_unique_patients")
query.addOrderBy("icd_code")
query.addOrderBy("admit_year")
query.addOrderBy("num_unique_patients DESC")
# Execute query.
results = DBUtil.execute(query)
def add_sim_case_column_to(csv):
query = SQLQuery()
query.addSelect("sim_patient_id")
query.addSelect("sim_case_name as sim_case")
query.addFrom("sim_grading_key sgk")
query.addJoin(
"sim_patient_order spo",
"sgk.clinical_item_id = spo.clinical_item_id and spo.sim_state_id = sgk.sim_state_id"
)
query.addGroupBy("sim_patient_id")
query.addGroupBy("sim_case_name")
case_names = DBUtil.execute(str(query))
# merge sim_case column
csv = pd.merge(csv,
pd.DataFrame(case_names,
columns=['sim_patient_id', 'sim_case']),
left_on='patient',
right_on='sim_patient_id')
return csv
def test_draw(self):
# Query events by clinical_item_category.
# SELECT
# pi.patient_id AS sequence_id,
# pi.item_date AS event_time,
# cic.description AS event_id
# FROM
# patient_item AS pi
# JOIN
# clinical_item AS ci
# ON
# pi.clinical_item_id = ci.clinical_item_id
# JOIN
# clinical_item_category AS cic
# ON
# ci.clinical_item_category_id = cic.clinical_item_category_id
# ORDER BY
# sequence_id,
# event_time,
# event_id
query = SQLQuery()
query.addSelect('pi.patient_id AS sequence_id')
query.addSelect('pi.item_date AS event_time')
query.addSelect('cic.description AS event_id')
query.addFrom('patient_item AS pi')
query.addJoin('clinical_item AS ci',
'pi.clinical_item_id = ci.clinical_item_id')
query.addJoin(
'clinical_item_category AS cic',
'ci.clinical_item_category_id = cic.clinical_item_category_id')
query.addOrderBy('sequence_id')
query.addOrderBy('event_time')
query.addOrderBy('event_id')
events = DBUtil.execute(query)
# Build graph based on clinical_item_category.
categoryDigraph = EventDigraph(events)
categoryDigraphVizFileName = "test-category-digraph.png"
categoryDigraph.draw(categoryDigraphVizFileName)
# Find all clinical_item_ids associated with each patient_id
# SELECT * FROM clinical_item WHERE clinical_item_category_id = 161;
DATA_QUERY = SQLQuery()
# From patient_item or clinical_item
DATA_QUERY.addSelect("patient_id")
DATA_QUERY.addSelect("clinical_item_category_id")
DATA_QUERY.addSelect('name')
DATA_QUERY.addSelect("description")
# Join
DATA_QUERY.addFrom("patient_item")
DATA_QUERY.addJoin(
"clinical_item",
"patient_item.clinical_item_id = clinical_item.clinical_item_id",
joinType="INNER")
DATA_QUERY.addWhereEqual("clinical_item_category_id = 161 AND description",
"Tt Med Univ (Primary)") # Everyone
#DATA_QUERY.addWhereEqual("clinical_item_category_id = 161 AND description", "Tt Pamf Med (Primary)") # Expert
DATA_QUERY.addOrderBy("patient_id", dir="ASC")
print(DATA_QUERY)
# Write out data to CSV
DBUtil.runDBScript(SCRIPT_FILE, False)
results = DBUtil.execute(DATA_QUERY)
unique_patient_ids = {}
def grade_cases(self, sim_patient_ids, sim_grader_id, conn=None):
"""Given the identifiers for a bunch of simulated physician-patient case
records, and the identifier for a particular grading key to use,
calculate what grade each case would get based on the choices made
and return a dictionary of case grades (keyed by the case ID).
"""
ext_conn = True
if conn is None:
conn = self.connFactory.connection()
ext_conn = False
try:
# Inner query retrieves physician-patient cases with ranking group_names (to later select first)
# per case for specified cases. Each NULL group_name is treated as a separate group by assigning it
# sim_patient_order_id. It also omits Default user (sim_user_id = 0) from grading.
inner_query = SQLQuery()
inner_query.addSelect("score")
inner_query.addSelect(
"rank() over (" # ranks rows incrementally in the same group
" partition by coalesce(group_name, sim_patient_order_id::text), sim_patient_id"
" order by sim_patient_order_id"
")")
inner_query.addSelect("sim_user_id")
inner_query.addSelect("sim_patient_id")
inner_query.addSelect("sim_grader_id")
inner_query.addFrom("sim_patient_order spo")
inner_query.addJoin(
"sim_grading_key sgk",
"sgk.clinical_item_id = spo.clinical_item_id"
" and sgk.sim_state_id = spo.sim_state_id")
inner_query.addWhereEqual("sgk.sim_grader_id", sim_grader_id)
inner_query.addWhereNotEqual(
"spo.sim_user_id",
0) # 0 = ignore 'Default user', sets up initial cases
inner_query.addWhereIn("spo.sim_patient_id", sim_patient_ids)
inner_query.addOrderBy("relative_time_start")
inner_query.addOrderBy("sim_patient_order_id")
# Outer query sums the score per patient case and selects most graded physician for the case.
# Theoretically, it isn't necessarily the most active physician for the case since his orders
# might have been dropped by selecting only the first record within group_name group.
query = SQLQuery()
query.addSelect("sim_patient_id")
query.addSelect("sim_grader_id")
query.addSelect("sum(score) as total_score")
query.addSelect(
"mode() within group (" # mode() selects most frequent value within group
" order by sim_user_id"
") as most_graded_user_id")
query.addFrom("(" + str(inner_query) + ") as ranked_groups")
query.addWhereEqual("ranked_groups.rank",
1) # count only first order in the same group
query.addGroupBy("sim_patient_id")
query.addGroupBy("sim_grader_id")
query_params = inner_query.getParams() + query.getParams()
grades_table = DBUtil.execute(query,
query_params,
includeColumnNames=True,
conn=conn)
grades_model = modelListFromTable(grades_table)
# get most active users for the cases
most_active_user_query = SQLQuery()
most_active_user_query.addSelect("sim_patient_id")
most_active_user_query.addSelect("mode() within group ("
" order by sim_user_id"
") as most_active_user_id")
most_active_user_query.addFrom("sim_patient_order")
most_active_user_query.addWhereNotEqual("sim_user_id",
0) # ignore Default user
most_active_user_query.addWhereIn("sim_patient_id",
sim_patient_ids)
most_active_user_query.addGroupBy("sim_patient_id")
most_active_user_query.addOrderBy("sim_patient_id")
most_active_user_table = DBUtil.execute(most_active_user_query,
includeColumnNames=True,
conn=conn)
most_active_user_model = modelListFromTable(most_active_user_table)
# make a dict by sim_patient_id out of results - will be used for combining
most_active_user_dict = {
most_active_user["sim_patient_id"]: most_active_user
for most_active_user in most_active_user_model
}
# combine results
complete_grades = [
grade.update(most_active_user_dict[grade["sim_patient_id"]])
for grade in grades_model
]
return complete_grades
finally:
if not ext_conn:
conn.close()
def test_init(self):
# Query events by clinical_item_category.
# SELECT
# pi.patient_id AS sequence_id,
# pi.item_date AS event_time,
# cic.description AS event_id
# FROM
# patient_item AS pi
# JOIN
# clinical_item AS ci
# ON
# pi.clinical_item_id = ci.clinical_item_id
# JOIN
# clinical_item_category AS cic
# ON
# ci.clinical_item_category_id = cic.clinical_item_category_id
# ORDER BY
# sequence_id,
# event_time,
# event_id
query = SQLQuery()
query.addSelect('pi.patient_id AS sequence_id')
query.addSelect('pi.item_date AS event_time')
query.addSelect('cic.description AS event_id')
query.addFrom('patient_item AS pi')
query.addJoin('clinical_item AS ci',
'pi.clinical_item_id = ci.clinical_item_id')
query.addJoin(
'clinical_item_category AS cic',
'ci.clinical_item_category_id = cic.clinical_item_category_id')
query.addOrderBy('sequence_id')
query.addOrderBy('event_time')
query.addOrderBy('event_id')
events = DBUtil.execute(query)
# Build graph based on clinical_item_category.
categoryDigraph = EventDigraph(events)
# Sort for easier comparison against test data.
actualCategoryNodes = sorted(categoryDigraph.nodes())
actualCategoryEdges = sorted(categoryDigraph.edges())
# Validate results.
expectedCategoryNodes = ED_TEST_OUTPUT_TABLES['test_init'][
'category_nodes']
self.assertEqualList(actualCategoryNodes, expectedCategoryNodes)
expectedCategoryEdges = ED_TEST_OUTPUT_TABLES['test_init'][
'category_edges']
self.assertEqualList(actualCategoryEdges, expectedCategoryEdges)
# Query events by clinical_item.
# SELECT
# pi.patient_id AS sequence_id,
# pi.item_date AS event_time,
# ci.description AS event_id
# FROM
# patient_item AS pi
# JOIN
# clinical_item AS ci
# ON
# pi.clinical_item_id = ci.clinical_item_id
# ORDER BY
# sequence_id,
# event_time,
# event_id
query = SQLQuery()
query.addSelect('pi.patient_id AS sequence_id')
query.addSelect('pi.item_date AS event_time')
query.addSelect('ci.description AS event_id')
query.addFrom('patient_item AS pi')
query.addJoin('clinical_item AS ci',
'pi.clinical_item_id = ci.clinical_item_id')
query.addJoin(
'clinical_item_category AS cic',
'ci.clinical_item_category_id = cic.clinical_item_category_id')
query.addOrderBy('sequence_id')
query.addOrderBy('event_time')
query.addOrderBy('event_id')
events = DBUtil.execute(query)
# Build graph based on clinical_item.
itemDigraph = EventDigraph(events)
# Sort for easier comparison against test data.
actualItemNodes = sorted(itemDigraph.nodes())
actualItemEdges = sorted(itemDigraph.edges())
# Validate results.
expectedItemNodes = ED_TEST_OUTPUT_TABLES['test_init']['item_nodes']
self.assertEqualList(actualItemNodes, expectedItemNodes)
expectedItemEdges = ED_TEST_OUTPUT_TABLES['test_init']['item_edges']
self.assertEqualList(actualItemEdges, expectedItemEdges)
