def queryItems(self, options, outputFile):
"""Query for all clinical item records that fulfill the options criteria
and then send the results as tab-delimited output to the outputFile.
"""
pauseSeconds = float(options.pauseSeconds)
query = SQLQuery()
query.addSelect("count(order_med_id_coded) as nOrders")
query.addSelect("om.med_route, om.medication_id, om.med_description")
query.addFrom("starr_datalake2018.order_med as om")
if options.descriptionPrefix:
query.addWhereOp("om.med_description", "like",
options.descriptionPrefix + "%%")
# Add wildcard to enabe prefix search
if options.medRoutes:
query.addWhereIn("om.med_route", options.medRoutes.split(","))
query.addGroupBy("om.medication_id, om.med_description, om.med_route")
query.addOrderBy("nOrders desc, om.med_description")
formatter = TextResultsFormatter(outputFile)
prog = ProgressDots()
for row in DBUtil.execute(query,
includeColumnNames=True,
connFactory=self.connFactory):
formatter.formatTuple(row)
time.sleep(pauseSeconds)
prog.update()
prog.printStatus()
def queryItems(self, options, outputFile):
"""Query for all clinical item records that fulfill the options criteria
and then send the results as tab-delimited output to the outputFile.
"""
pauseSeconds = float(options.pauseSeconds)
query = SQLQuery()
query.addSelect(
"cic.description, ci.clinical_item_id, ci.name, ci.description")
query.addFrom("clinical_item_category as cic")
query.addFrom("clinical_item as ci")
query.addWhere(
"cic.clinical_item_category_id = ci.clinical_item_category_id")
if options.itemPrefix:
query.addWhereOp("ci.description", "like",
options.itemPrefix + "%%")
# Add wildcard to enabe prefix search
if options.categoryNames:
query.addWhereIn("cic.description",
options.categoryNames.split(","))
query.addOrderBy(
"cic.description, ci.name, ci.description, ci.clinical_item_id")
formatter = TextResultsFormatter(outputFile)
prog = ProgressDots()
for row in DBUtil.execute(query,
includeColumnNames=True,
connFactory=self.connFactory):
formatter.formatTuple(row)
time.sleep(pauseSeconds)
prog.update()
prog.printStatus()
def parseLabResultsFile(labFile):
log.info("Parse lab results file");
prog = ProgressDots();
labsByBaseNameByPatientId = dict(); # Dictionary of dictionaries of lists of result items
for labResult in TabDictReader(labFile):
if labResult["ord_num_value"] is not None and labResult["ord_num_value"] != NULL_STRING:
patientId = int(labResult["pat_id"]);
labBaseName = labResult["base_name"];
resultValue = float(labResult["ord_num_value"]);
resultTime = DBUtil.parseDateValue(labResult["result_time"]);
if resultValue < LAB_SENTINEL_VALUE: # Skip apparent placeholder values
labResult["pat_id"] = labResult["patient_id"] = patientId;
labResult["ord_num_value"] = resultValue;
labResult["result_time"] = resultTime;
if patientId not in labsByBaseNameByPatientId:
labsByBaseNameByPatientId[patientId] = dict();
if labBaseName not in labsByBaseNameByPatientId[patientId]:
labsByBaseNameByPatientId[patientId][labBaseName] = list();
labsByBaseNameByPatientId[patientId][labBaseName].append( labResult );
prog.update();
prog.printStatus();
return labsByBaseNameByPatientId;
def addLabFeatures(labsByBaseNameByPatientId, patientById, colNames, indexItemBaseName, labBaseNames, labPreTime, labPostTime):
log.info("Sort lab results by result time for each patient and find items within specified time period to aggregate");
prog = ProgressDots();
for iPatient, (patientId, labsByBaseName) in enumerate(labsByBaseNameByPatientId.iteritems()):
# Look for the first result of the index item (ferritin)
indexItem = None;
if indexItemBaseName in labsByBaseName:
for labResult in labsByBaseName[indexItemBaseName]:
if indexItem is None or labResult["result_time"] < indexItem["result_time"]:
indexItem = labResult;
if indexItem is not None: # Skip this patient if no index item found, should not be possible since pre-screened for relevant patients
indexTime = indexItem["result_time"];
patient = patientById[patientId];
patient["index_time"] = indexTime;
preTimeLimit = indexTime+labPreTime;
postTimeLimit = indexTime+labPostTime;
# Init values for each lab of interest to an empty list
for labBaseName in labBaseNames:
# Default to null for all values
patient["%s.min" % labBaseName] = None;
patient["%s.max" % labBaseName] = None;
patient["%s.median" % labBaseName] = None;
patient["%s.mean" % labBaseName] = None;
patient["%s.std" % labBaseName] = None;
patient["%s.first" % labBaseName] = None;
patient["%s.last" % labBaseName] = None;
patient["%s.proximate" % labBaseName] = None;
proximateValue = None;
if labBaseName in labsByBaseName: # Not all patients will have all labs checked
proximateItem = None; # Item closest to the index item in time
valueList = list();
for labResult in labsByBaseName[labBaseName]:
resultTime = labResult["result_time"];
if preTimeLimit <= resultTime and resultTime < postTimeLimit:
# Occurs within time frame of interest, so record this value
valueList.append(labResult["ord_num_value"]);
if proximateItem is None or (abs(resultTime-indexTime) < abs(proximateItem["result_time"]-indexTime)):
proximateItem = labResult;
proximateValue = proximateItem["ord_num_value"];
if len(valueList) > 0:
patient["%s.min" % labBaseName] = np.min(valueList);
patient["%s.max" % labBaseName] = np.max(valueList);
patient["%s.median" % labBaseName] = np.median(valueList);
patient["%s.mean" % labBaseName] = np.mean(valueList);
patient["%s.std" % labBaseName] = np.std(valueList);
patient["%s.first" % labBaseName] = valueList[0]; # Assumes previously sorted
patient["%s.last" % labBaseName] = valueList[-1]; # by result_time
patient["%s.proximate" % labBaseName] = proximateValue;
prog.update();
colNames.extend(colsFromLabBaseNames(labBaseNames));
prog.printStatus();
def main(argv):
"""Main method, callable from command line"""
usageStr = "usage: %prog -p <pid> -c <commandStr>\n"
parser = OptionParser(usage=usageStr)
parser.add_option(
"-p",
"--pid",
dest="pid",
help=
"Process ID to monitor. As soon as it is no longer found by Unix signal (os.kill(pid,0)), will proceed to execute the given command"
)
parser.add_option(
"-i",
"--interval",
dest="interval",
default="1",
help=
"How many seconds to wait before checking if the PID is still active. Default to 1 second."
)
parser.add_option(
"-c",
"--commandStr",
dest="commandStr",
help=
"Command string to execute after the specified PID is longer found. Will just pass whole string to subprocess"
)
(options, args) = parser.parse_args(argv[1:])
print >> sys.stderr, "Starting: " + str.join(" ", argv)
timer = time.time()
if options.pid and options.commandStr:
pid = int(options.pid)
interval = float(options.interval)
prog = ProgressDots(60, 1, "intervals")
while pidExists(pid):
time.sleep(interval)
prog.update()
prog.printStatus()
print >> sys.stderr, "Executing: ", options.commandStr
process = subprocess.Popen(options.commandStr)
print >> sys.stderr, "Started process: ", process.pid
else:
parser.print_help()
sys.exit(-1)
timer = time.time() - timer
print >> sys.stderr, ("%.3f seconds to complete" % timer)
def main(argv):
conversionProcessor = STRIDEOrderResultsConversion();
conn = DBUtil.connection();
try:
# Pull out list of result names to look for that are not already in the calculated
nameTable = DBUtil.execute("select name from sim_result except select base_name from order_result_stat", conn=conn);
prog = ProgressDots(big=1,small=1,total=len(nameTable));
for row in nameTable:
baseName = row[0];
print("Calculating Stats for %s" % baseName, file=sys.stderr);
statModel = conversionProcessor.calculateResultStats( baseName, conn=conn );
DBUtil.insertRow("order_result_stat", statModel, conn=conn );
prog.update();
prog.printStatus();
conn.commit();
finally:
conn.close();
def parseClinicalItemFile(itemFile, patientIdCol="patient_id", timeCol="item_date"):
prog = ProgressDots();
itemTimesByPatientId = dict();
for itemData in TabDictReader(itemFile):
patientId = int(itemData[patientIdCol]);
itemTime = DBUtil.parseDateValue(itemData[timeCol]);
itemData[patientIdCol] = patientId;
itemData[timeCol] = itemTime;
if patientId not in itemTimesByPatientId:
itemTimesByPatientId[patientId] = list();
itemTimesByPatientId[patientId].append( itemTime );
prog.update();
prog.printStatus();
return itemTimesByPatientId;
def main_quickTest(argv):
modelFilename = argv[1]
modeler = TopicModel()
timer = time.time()
(model, docCountByWordId) = modeler.loadModelAndDocCounts(modelFilename)
timer = time.time() - timer
log.info("%.2f seconds to load", timer)
timer = time.time()
weightByItemIdByTopicId = modeler.generateWeightByItemIdByTopicId(
model, 100)
timer = time.time() - timer
log.info("%.2f seconds to generate weights", timer)
for i in xrange(3):
prog = ProgressDots()
for (topicId, weightByItemId) in weightByItemIdByTopicId.iteritems():
for (itemId, itemWeight) in weightByItemId.iteritems():
prog.update()
prog.printStatus()
"""
def queryPatientEpisodes(outputFile, extractor):
log.info(
"Select patient admissions with provider category of Tt Pamf Med (Primary) or Tt Med Univ (Primary)"
)
conn = DBUtil.connection()
cursor = conn.cursor()
try:
# # Clinical item category for admission diagnoses
# # ADMIT_DX_CATEGORY_ID = 2;
# admitDxCategoryId = DBUtil.execute("select clinical_item_category_id from clinical_item_category where description like '%%ADMIT_DX%%'", conn=conn)[0][0];
# # Look for items indicating suspected infection / sepsis
# ivAntibioticItemIds = loadIVAntibioticItemIds(extractor);
# bloodCultureItemIds = loadBloodCultureItemIds(extractor);
# respiratoryViralPanelItemIds = loadRespiratoryViralPanelItemIds(extractor);
# # Merge IV antibiotics and blood cultures, respiratory panels as items that suggest sepsis is suspected
# suspectSepsisItemIds = ivAntibioticItemIds.union(bloodCultureItemIds.union(respiratoryViralPanelItemIds));
# suspectSepsisItemIdsStr = str.join(',', [str(itemId) for itemId in suspectSepsisItemIds]); # Convert to comma-separated string via a str.join function on list contracture
# # Look for primary surgery teams to exclude
# excludeTeamCategory = "SurgerySpecialty";
# excludeTreatmentTeams = list();
# for row in extractor.loadMapData("TreatmentTeamGroups"):
# if row["team_category"] == excludeTeamCategory:
# excludeTreatmentTeams.append(row["treatment_team"]);
# query = SQLQuery();
# query.addSelect("clinical_item_id");
# query.addFrom("clinical_item");
# query.addWhereIn("description", excludeTreatmentTeams );
# excludeTeamItemIds = set();
# for row in DBUtil.execute(query, conn=conn):
# excludeTeamItemIds.add(row[0]);
# excludeTeamItemIdsStr = str.join(',', [str(itemId) for itemId in excludeTeamItemIds]); # Convert to comma-separated string via a str.join function on list contracture
# First pass query to get the list of patients and emergency department presentation times
cohortQuery = \
"""
select adt1.pat_anon_id, adt1.pat_enc_csn_anon_id, adt1.shifted_transf_in_dt_tm as edAdmitTime, adt2.shifted_transf_out_dt_tm as dischargeTime
from stride_adt as adt1, stride_adt as adt2
where
adt1.pat_anon_id in
(select patient_id from patient_item inner join clinical_item on patient_item.clinical_item_id = clinical_item.clinical_item_id where clinical_item.clinical_item_category_id = 161 AND clinical_item.description = '%s')
and adt1.pat_enc_csn_anon_id = adt2.pat_enc_csn_anon_id
""" % ("Tt Pamf Med (Primary)")
print(cohortQuery, file=sys.stderr)
cursor.execute(cohortQuery)
patientEpisodes = list()
patientEpisodeById = dict()
# Collect Build basic patient ID and
# ED presentation dates and Discharge date/time
prog = ProgressDots()
row = cursor.fetchone()
while row is not None:
(patientId, encounterId, edAdmitTime, dischargeTime) = row
#patientId = int(patientId);
patientEpisode = \
RowItemModel \
( { "patient_id":patientId,
"edAdmitTime":edAdmitTime,
"dischargeTime":dischargeTime,
"encounter_id":encounterId,
"payorTitle": None, # Default encounter data to null in case can't find it later
"bpSystolic": None,
"bpDiastolic": None,
"temperature": None,
"pulse": None,
"respirations": None,
}
)
patientEpisodes.append(patientEpisode)
if patientEpisode["encounter_id"] not in patientEpisodeById:
patientEpisodeById[
patientEpisode["encounter_id"]] = patientEpisode
prog.update()
row = cursor.fetchone()
prog.printStatus()
# Second query phase to link to encounter information (e.g., insurance, admitting vital signs)
encounterIds = columnFromModelList(patientEpisodes, "encounter_id")
query = SQLQuery()
query.addSelect("pat_id")
query.addSelect("pat_enc_csn_id")
query.addSelect("title")
query.addSelect("bp_systolic")
query.addSelect("bp_diastolic")
query.addSelect("temperature")
query.addSelect("pulse")
query.addSelect("respirations")
query.addFrom("stride_patient_encounter")
query.addWhereIn("pat_enc_csn_id", encounterIds)
cursor.execute(str(query), query.params)
row = cursor.fetchone()
while row is not None:
(patientId, encounterId, payorTitle, bpSystolic, bpDiastolic,
temperature, pulse, respirations) = row
if encounterId in patientEpisodeById:
patientEpisode = patientEpisodeById[encounterId]
if patientEpisode["payorTitle"] is None:
patientEpisode["payorTitle"] = set()
# Single encounters may have multiple payors to track
patientEpisode["payorTitle"].add(payorTitle)
patientEpisode["bpSystolic"] = bpSystolic
patientEpisode["bpDiastolic"] = bpDiastolic
patientEpisode["temperature"] = temperature
patientEpisode["pulse"] = pulse
patientEpisode["respirations"] = respirations
row = cursor.fetchone()
# Drop results as tab-delimited text output
formatter = TextResultsFormatter(outputFile)
formatter.formatResultDicts(patientEpisodes, addHeaderRow=True)
return patientEpisodes
finally:
cursor.close()
conn.close()
# Select the second (index one) form (the first form is a search query box)
#br.select_form(nr=1)
# User credentials, single login and then store a cookie
#br.form['username'] = '******'
#br.form['password'] = '******'
# Login
#br.submit()
# Base URL to query for, with parameters for subsets
BASE_URL = 'http://www.samhsa.gov/medication-assisted-treatment/physician-program-data/treatment-physician-locator?field_bup_physician_us_state_value=All&page=%s'
BASE_OUTPUT_FILENAME = 'buprenorphinePhysicians.%s.htm';
N_PAGES = 641
#N_PAGES = 10
progress = ProgressDots(big=100,small=2);
for iPage in xrange(N_PAGES):
sourceURL = BASE_URL % (iPage);
sourceFile = br.open(sourceURL);
sourceContent = sourceFile.read(); # Just store whole file in memory for simplicity
sourceFile.close();
localFilename = BASE_OUTPUT_FILENAME % (iPage);
localFile = open(localFilename,"w");
localFile.write(sourceContent);
localFile.close();
progress.update();
progress.printStatus();
for procCode, description, count in subResults:
print str.join(
"\t", [procCode, description, lastBaseName, lastComponentName])
orderProcIds.clear()
# Reset once done
nIds = 0
lastBaseName = baseName
lastComponentName = componentName
if nIds < SAMPLE_SIZE: # Don't track all, just the first sample
orderProcIds.add(orderProcId)
nIds += 1
row = cursor.fetchone()
prog.update()
# Final base name to query off of
subQuery = """
select proc_code, description, count(order_proc_id)
from stride_order_proc
where order_proc_id in (%s)
group by proc_code, description
""" % generatePlaceholders(nIds)
subResults = DBUtil.execute(subQuery, tuple(orderProcIds), conn=conn)
for procCode, description, count in subResults:
print str.join("\t",
[procCode, description, lastBaseName, lastComponentName])
prog.printStatus()
def main(argv):
# Initial pass to get single diagnosis baselines by looking for 2x Dx combos where Dx1 = Dx2
countByDx = dict()
countByRxDx = dict()
associationFile = open(argv[1])
associationFile.readline()
# Dump header row
for line in associationFile:
line.strip()
chunks = line.split("\t")
rx = chunks[0]
dx1 = chunks[1]
dx2 = chunks[2]
if dx1 == dx2:
rxDxCount = int(chunks[3])
rxCount = int(chunks[4])
dxCount = int(chunks[5])
countByRxDx[(rx, dx1)] = rxDxCount
countByDx[dx1] = dxCount
# Second pass to now do stats for combo diagnoses, to see if prescription shows difference between 1 or both diagnoses
statIds = (
"P-Fisher",
"P-YatesChi2",
"oddsRatio",
"relativeRisk",
"interest",
"LR+",
"LR-",
"sensitivity",
"specificity",
"PPV",
"NPV",
)
headerCols = [
"Rx",
"Dx1",
"Dx2",
"RxDx1Dx2Count",
"RxDx1Count",
"RxDx2Count",
"RxCount",
"Dx1Dx2Count",
"Total",
"E(RxDx1Dx2Count)",
"E(RxDx2Dx1Count)",
"E(Dx1Dx2Count)",
"P-Chi2-Obs:Exp",
]
headerCols.extend(statIds)
headerStr = str.join("\t", headerCols)
print(headerStr)
associationFile = open(argv[1])
associationFile.readline()
# Dump header row
progress = ProgressDots()
for line in associationFile:
line.strip()
chunks = line.split("\t")
rx = chunks[0]
dx1 = chunks[1]
dx2 = chunks[2]
rxDx1Dx2Count = int(chunks[3])
rxDx1Count = countByRxDx[(rx, dx1)]
rxDx2Count = countByRxDx[(rx, dx2)]
rxCount = int(chunks[4])
dx1Count = countByDx[dx1]
dx2Count = countByDx[dx2]
dx1dx2Count = int(chunks[5])
totalCount = float(chunks[6])
# Floating point to auto-convert float divisions later
conStats = ContingencyStats(rxDx1Dx2Count, rxCount, dx1dx2Count,
totalCount)
# Expected vs. observed Rx rates dependent on presence of Dx1Dx2 combination based one whether Rx rates per diagnosis are independent of combination
observed = array \
([ (rxDx1Dx2Count), (dx1dx2Count-rxDx1Dx2Count),
(rxCount-rxDx1Dx2Count), (totalCount-dx1dx2Count-rxCount+rxDx1Dx2Count),
])
# Expected rates based on assumption that diagnoses occur independently of one another
expectedRxDx1Dx2 = (rxDx1Count * dx2Count / totalCount)
expectedRxDx2Dx1 = (rxDx2Count * dx1Count / totalCount)
expectedDx1Dx2 = (dx1Count * dx2Count / totalCount)
expected = array \
([ (expectedRxDx1Dx2), (expectedDx1Dx2-expectedRxDx1Dx2),
(rxCount-expectedRxDx1Dx2), (totalCount-expectedDx1Dx2-rxCount+expectedRxDx1Dx2),
])
(chi2ObsExp, pChi2ObsExp) = chisquare(observed, expected)
dataCells = [
rx, dx1, dx2, rxDx1Dx2Count, rxDx1Count, rxDx2Count, rxCount,
dx1dx2Count, totalCount, expectedRxDx1Dx2, expectedRxDx2Dx1,
expectedDx1Dx2, pChi2ObsExp
]
for statId in statIds:
try:
dataCells.append(conStats[statId])
except ZeroDivisionError:
dataCells.append(None)
for i, value in enumerate(dataCells):
dataCells[i] = str(value)
# String conversion to allow for concatenation below
dataStr = str.join("\t", dataCells)
print(dataStr)
progress.update()
progress.printStatus()
];
for categoryName in CATEGORY_LIST:
for itemPrefix in ITEM_PREFIXES:
specificArgv = ["-c", categoryName, "-i", itemPrefix, "results/queryResults.%s.%s.tab.gz" % (itemPrefix, categoryName) ];
specificArgvList.append(specificArgv);
prog = ProgressDots(1,1,"Processes",total=len(specificArgvList));
for specificArgv in specificArgvList:
key = "%.3d.%s" % (prog.getCounts(), str.join("_",specificArgv) );
key = key.replace("/",".."); # Don't want to use directory separator in temp log file name
argv = list(baseArgv)
argv.extend(specificArgv);
log.info( "Starting: "+str.join(" ", argv) );
logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
# Blocking sub-process call if want serial processes.
#subprocess.call(argv, stderr=logFile);
# Non-blocking subprocess.Popen to spawn parallel processes
process = subprocess.Popen(argv, stderr=logFile);
log.info("Process ID: %s" % process.pid);
# Print command lines to effectively generate a .sh script
#print "nohup",
#print str.join(" ", argv),
#print "&>", LOG_FILE_TEMPLATE % key,"&"
prog.update();
prog.printStatus();
def queryPatientEpisodes(outputFile, extractor):
log.info("Select patient admissions with possible/probable sepsis within 24 hours of admission (long query >60 min?)...");
conn = DBUtil.connection();
cursor = conn.cursor();
try:
# Clinical item category for admission diagnoses
# ADMIT_DX_CATEGORY_ID = 2;
admitDxCategoryId = DBUtil.execute("select clinical_item_category_id from clinical_item_category where description like '%%ADMIT_DX%%'", conn=conn)[0][0];
# Look for items indicating suspected infection / sepsis
ivAntibioticItemIds = loadIVAntibioticItemIds(extractor);
bloodCultureItemIds = loadBloodCultureItemIds(extractor);
respiratoryViralPanelItemIds = loadRespiratoryViralPanelItemIds(extractor);
# Merge IV antibiotics and blood cultures, respiratory panels as items that suggest sepsis is suspected
suspectSepsisItemIds = ivAntibioticItemIds.union(bloodCultureItemIds.union(respiratoryViralPanelItemIds));
suspectSepsisItemIdsStr = str.join(',', [str(itemId) for itemId in suspectSepsisItemIds]); # Convert to comma-separated string via a str.join function on list contracture
# Look for primary surgery teams to exclude
excludeTeamCategory = "SurgerySpecialty";
excludeTreatmentTeams = list();
for row in extractor.loadMapData("TreatmentTeamGroups"):
if row["team_category"] == excludeTeamCategory:
excludeTreatmentTeams.append(row["treatment_team"]);
query = SQLQuery();
query.addSelect("clinical_item_id");
query.addFrom("clinical_item");
query.addWhereIn("description", excludeTreatmentTeams );
excludeTeamItemIds = set();
for row in DBUtil.execute(query, conn=conn):
excludeTeamItemIds.add(row[0]);
excludeTeamItemIdsStr = str.join(',', [str(itemId) for itemId in excludeTeamItemIds]); # Convert to comma-separated string via a str.join function on list contracture
# First pass query to get the list of patients and emergency department presentation times
cohortQuery = \
"""
-- Pick out date(s) when admitted through emergency department and matching discharge time
select adt1.pat_anon_id, adt1.pat_enc_csn_anon_id, adt1.shifted_transf_in_dt_tm as edAdmitTime, adt2.shifted_transf_out_dt_tm as dischargeTime
from stride_adt as adt1, stride_adt as adt2
where
-- Admission event
adt1.department_in = 'EMERGENCY DEPARTMENT' and
adt1.event_in = 'Admission' and
adt1.pat_anon_id in
( -- Select any patient with any suspected sepsis related order (i.e., IV antibiotics or blood cultures)
select patient_id
from patient_item as pi
where pi.clinical_item_id in (%s)
except
-- Exclude any patient who has been on a primary surgery team
select patient_id
from patient_item
where clinical_item_id in (%s)
-- -12434586418575,-12432455207729,-12428492282572,-12428492282572,-12424048595257,-12414081679705
) and
adt1.pat_enc_csn_anon_id = adt2.pat_enc_csn_anon_id and
-- Discharge event
adt2.event_out = 'Discharge'
order by adt1.shifted_transf_in_dt_tm
""" % (suspectSepsisItemIdsStr, excludeTeamItemIdsStr);
print >> sys.stderr, cohortQuery;
cursor.execute(cohortQuery);
patientEpisodes = list();
patientEpisodeById = dict();
# Collect Build basic patient ID and
# ED presentation dates and Discharge date/time
prog = ProgressDots();
row = cursor.fetchone();
while row is not None:
(patientId, encounterId, edAdmitTime, dischargeTime) = row;
#patientId = int(patientId);
patientEpisode = \
RowItemModel \
( { "patient_id":patientId,
"edAdmitTime":edAdmitTime,
"dischargeTime":dischargeTime,
"encounter_id":encounterId,
"payorTitle": None, # Default encounter data to null in case can't find it later
"bpSystolic": None,
"bpDiastolic": None,
"temperature": None,
"pulse": None,
"respirations": None,
}
);
patientEpisodes.append(patientEpisode);
if patientEpisode["encounter_id"] not in patientEpisodeById:
patientEpisodeById[patientEpisode["encounter_id"]] = patientEpisode;
prog.update();
row = cursor.fetchone();
prog.printStatus();
# Second query phase to link to encounter information (e.g., insurance, admitting vital signs)
encounterIds = columnFromModelList(patientEpisodes, "encounter_id");
query = SQLQuery();
query.addSelect("pat_id");
query.addSelect("pat_enc_csn_id");
query.addSelect("title");
query.addSelect("bp_systolic");
query.addSelect("bp_diastolic");
query.addSelect("temperature");
query.addSelect("pulse");
query.addSelect("respirations");
query.addFrom("stride_patient_encounter");
query.addWhereIn("pat_enc_csn_id", encounterIds);
cursor.execute(str(query), query.params);
row = cursor.fetchone();
while row is not None:
(patientId, encounterId, payorTitle, bpSystolic, bpDiastolic, temperature, pulse, respirations) = row;
if encounterId in patientEpisodeById:
patientEpisode = patientEpisodeById[encounterId];
if patientEpisode["payorTitle"] is None:
patientEpisode["payorTitle"] = set(); # Single encounters may have multiple payors to track
patientEpisode["payorTitle"].add(payorTitle);
patientEpisode["bpSystolic"] = bpSystolic;
patientEpisode["bpDiastolic"] = bpDiastolic;
patientEpisode["temperature"] = temperature;
patientEpisode["pulse"] = pulse;
patientEpisode["respirations"] = respirations;
row = cursor.fetchone();
# Drop results as tab-delimited text output
formatter = TextResultsFormatter(outputFile);
formatter.formatResultDicts(patientEpisodes, addHeaderRow=True);
return patientEpisodes;
finally:
cursor.close();
conn.close();
def main(argv):
medIdsByActiveRx = dict()
medIdsByActiveRx['Buprenorphine'] = ('125498', '114474', '212560',
'114475', '114467', '114468')
medIdsByActiveRx['Fentanyl Patch'] = ('2680', '27908', '125379', '27905',
'27906', '540107', '540638',
'540101', '27907')
medIdsByActiveRx['Methadone'] = ('540483', '4953', '4951', '10546',
'214468', '15996', '41938', '4954',
'4952')
medIdsByActiveRx['Hydrocodone'] = ('3724', '4579', '8576', '8577', '8951',
'10204', '12543', '13040', '14963',
'14965', '14966', '17061', '17927',
'19895', '20031', '28384', '29486',
'29487', '34505', '34544', '35613',
'117862', '204249', '206739')
medIdsByActiveRx['Hydromorphone'] = ('2458', '2459', '2464', '2465',
'3757', '3758', '3759', '3760',
'3761', '10224', '10225', '10226',
'10227', '200439', '201094', '201096',
'201098', '540125', '540179',
'540666')
medIdsByActiveRx['Morphine'] = (
'5167', '5168', '5172', '5173', '5176', '5177', '5178', '5179', '5180',
'5183', '6977', '10655', '15852', '20908', '20909', '20910', '20914',
'20915', '20919', '20920', '20921', '20922', '29464', '30138', '31413',
'36140', '36141', '79691', '87820', '89282', '91497', '95244', '96810',
'112562', '112564', '115335', '115336', '126132', '198543', '198544',
'198623', '201842', '201848', '205011', '206731', '207949', '208896',
'540182', '540300')
medIdsByActiveRx['Oxycodone'] = ('5940', '5941', '6122', '6981', '10812',
'10813', '10814', '14919', '16121',
'16123', '16129', '16130', '19187',
'26637', '26638', '27920', '27921',
'27922', '27923', '28897', '28899',
'28900', '31851', '31852', '31863',
'31864', '92248', '126939', '200451',
'203690', '203691', '203692', '203705',
'203706', '203707', '204020', '204021')
query = baseQuery()
totalPatients = float(DBUtil.execute(query)[0][0])
# print"Total Patients\t%s" % totalPatients
# print"======= Dx Groups ===========";
# print"Dx Group\tPt Count\tDx Rate";
patientsPerDxGroup = dict()
query = SQLQuery()
query.addSelect("count(distinct prob.pat_id) as ptCount")
query.addSelect("prob.%s" % DX_COL)
query.addFrom("stride_problem_list as prob")
query.addWhereOp("prob.noted_date", "<", LIMIT_DATE)
query.addGroupBy("prob.%s" % DX_COL)
if DOUBLE_DX:
query.addSelect("prob2.%s" % DX_COL)
query.addFrom("stride_problem_list as prob2")
query.addWhere("prob.pat_id = prob2.pat_id")
query.addWhereOp("prob2.noted_date", "<", LIMIT_DATE)
query.addGroupBy("prob2.%s" % DX_COL)
results = DBUtil.execute(query)
for row in results:
patientCount = row[0]
dxGroup = row[1]
if DOUBLE_DX:
dxGroup = (dxGroup, row[2])
# Composite tuple including second diagnosis
patientsPerDxGroup[dxGroup] = patientCount
progress = ProgressDots()
for activeRx, medIds in medIdsByActiveRx.iteritems():
query = baseQuery()
query.addWhereIn("medication_id", medIds)
# Baseline prescription rates
rxPtCount = DBUtil.execute(query)[0][0]
# print"====== Rx Counts ======";
# print"Rx\tPt Count\tRx Rate";
# print"%s\t%s\t%s" % (activeRx, rxPtCount, (rxPtCount/totalPatients));
# print"======== Rx-Dx Association ========";
statIds = (
"P-Fisher",
"P-YatesChi2",
"oddsRatio",
"relativeRisk",
"interest",
"LR+",
"LR-",
"sensitivity",
"specificity",
"PPV",
"NPV",
)
if progress.getCounts() == 0:
headerCols = [
"Rx", "Dx", "RxDxCount", "RxCount", "DxCount", "Total"
]
if DOUBLE_DX:
headerCols.insert(2, "Dx2")
headerCols.extend(statIds)
headerStr = str.join("\t", headerCols)
print headerStr
# Query out per diagnosis group, but do as aggregate grouped query
query.addSelect("prob.%s" % DX_COL)
query.addFrom("stride_problem_list as prob")
query.addWhere("med.pat_id = prob.pat_id")
query.addWhereOp("prob.noted_date", "<", LIMIT_DATE)
#query.addWhereIn("prob.%s" % DX_COL, dxKeys );
query.addGroupBy("prob.%s" % DX_COL)
if DOUBLE_DX:
query.addSelect("prob2.%s" % DX_COL)
query.addFrom("stride_problem_list as prob2")
query.addWhere("prob.pat_id = prob2.pat_id")
query.addWhereOp("prob2.noted_date", "<", LIMIT_DATE)
query.addGroupBy("prob2.%s" % DX_COL)
results = DBUtil.execute(query)
for row in results:
rxDxPtCount = row[0]
dxGroup = row[1]
if DOUBLE_DX:
dxGroup = (dxGroup, row[2])
# Composite tuple including second diagnosis
dxPtCount = patientsPerDxGroup[dxGroup]
conStats = ContingencyStats(rxDxPtCount, rxPtCount, dxPtCount,
totalPatients)
dataCells = [
activeRx, dxGroup, rxDxPtCount, rxPtCount, dxPtCount,
totalPatients
]
if DOUBLE_DX:
dataCells[1] = dxGroup[0]
dataCells.insert(2, dxGroup[1])
for statId in statIds:
try:
dataCells.append(conStats[statId])
except ZeroDivisionError:
dataCells.append(None)
for i, value in enumerate(dataCells):
dataCells[i] = str(value)
# String conversion to allow for concatenation below
dataStr = str.join("\t", dataCells)
print dataStr
progress.update()
progress.printStatus()
