def action_default(self):
query = RecommenderQuery()
query.parseParams(self.requestData)
displayFields = query.getDisplayFields()
recommendedData = self.recommender(query)
if len(recommendedData) > 0:
# Denormalize results with links to clinical item descriptions
self.recommender.formatRecommenderResults(recommendedData)
# Format for HTML and add a control field for interaction with the data
for dataModel in recommendedData:
self.prepareResultRow(dataModel, displayFields)
# Display fields should append Format suffix to identify which version to display, but use original for header labels
(self.requestData["fieldHeaders"], displayFieldsFormatSuffixed
) = self.prepareDisplayHeaders(displayFields)
colNames = [
"controls", "rank", "name", "description", "category_description"
]
colNames.extend(displayFieldsFormatSuffixed)
formatter = HtmlResultsFormatter(StringIO(),
valign="middle",
align="center")
formatter.formatResultDicts(recommendedData, colNames)
self.requestData["dataRows"] = formatter.getOutFile().getvalue()
baseQueryStr = "&targetItemIds=&excludeItemIds=71052,71046,71054,71083,71045,71047&excludeCategoryIds=1,58,4,2,160,161,59,13,159,163,23,62,18,11,46,2&timeDeltaMax=86400&sortField=P-YatesChi2-NegLog&sortReverse=True&filterField1=prevalence<:&filterField2=PPV<:&filterField3=RR<:&filterField4=sensitivity<:&filterField5=P-YatesChi2<:&resultCount=4000&invertQuery=false&showCounts=true&countPrefix=patient_&aggregationMethod=weighted&cacheTime=0"
recommender = ItemAssociationRecommender()
diagnosis_count = 0
for line in diagnoses:
line = line.strip().split(",")
clinical_item_id = line[0]
description = " ".join(line[1:])
queryStr = "queryItemIds=" + str(clinical_item_id) + baseQueryStr
print('Finding Top Associations for "{0}"'.format(description))
# Build RecommenderQuery
query = RecommenderQuery()
paramDict = dict(urlparse.parse_qsl(queryStr, True))
query.parseParams(paramDict)
# Call ItemRecommender
recommendations = recommender(query)
# Output to csv file
description = description.replace("/", ";")
fname = str(clinical_item_id) + " " + str(description) + ".csv"
outfname = open(
"/Users/jwang/Desktop/Results/item_associations_expert_unmatched/" +
fname, "w")
outfname.write(
"clinical_item_id,description,score,PPV,OR,prevalence,RR,P-YatesChi2\n"
)
association_count = 0
def action_default(self):
"""Look for related orders by association / recommender methods"""
# If patient is specified then modify query and exclusion list based on items already ordered for patient
recentItemIds = set()
if self.requestData["sim_patient_id"]:
patientId = int(self.requestData["sim_patient_id"])
simTime = int(self.requestData["sim_time"])
# Track recent item IDs (orders, diagnoses, unlocked results, etc. that related order queries will be based off of)
manager = SimManager()
recentItemIds = manager.recentItemIds(patientId, simTime)
# Recommender Instance to test on
self.recommender = ItemAssociationRecommender()
self.recommender.dataManager.dataCache = webDataCache
# Allow caching of data for rapid successive queries
query = RecommenderQuery()
if self.requestData["sortField"] == "":
self.requestData["sortField"] = "P-YatesChi2-NegLog"
# P-Fisher-NegLog should yield better results, but beware, much longer to calculate
query.parseParams(self.requestData)
if len(query.excludeItemIds) == 0:
query.excludeItemIds = self.recommender.defaultExcludedClinicalItemIds(
)
if len(query.excludeCategoryIds) == 0:
query.excludeCategoryIds = self.recommender.defaultExcludedClinicalItemCategoryIds(
)
#query.fieldList.extend( ["prevalence","PPV","RR"] );
displayFields = list()
if self.requestData["displayFields"] != "":
displayFields = self.requestData["displayFields"].split(",")
# Exclude items already ordered for the patient from any recommended list
query.excludeItemIds.update(recentItemIds)
if not query.queryItemIds: # If no specific query items specified, then use the recent patient item IDs
query.queryItemIds.update(recentItemIds)
recommendedData = self.recommender(query)
if len(recommendedData) > 0:
# Denormalize results with links to clinical item descriptions
self.recommender.formatRecommenderResults(recommendedData)
# Display fields should append Format suffix to identify which version to display, but use original for header labels
(self.requestData["fieldHeaders"], displayFieldsFormatSuffixed
) = self.prepareDisplayHeaders(displayFields)
# Format for HTML and add a control field for interaction with the data
for dataModel in recommendedData:
self.prepareResultRow(dataModel, displayFields)
# Try organize by category
if self.requestData["groupByCategory"]:
recommendedData = self.recommender.organizeByCategory(
recommendedData)
colNames = ["controls"]
# "name" for code. ,"category_description"
colNames.extend(displayFieldsFormatSuffixed)
colNames.extend(["description"])
lastModel = None
htmlLines = list()
for dataModel in recommendedData:
newCategory = (lastModel is None
or lastModel["category_description"] !=
dataModel["category_description"])
showCategory = (self.requestData["groupByCategory"]
and newCategory)
# Limit category display if many repeats
if showCategory:
htmlLines.append(CATEGORY_HEADER_TEMPLATE % dataModel)
htmlLines.append(
self.formatRowHTML(dataModel, colNames, showCategory))
lastModel = dataModel
self.requestData["dataRows"] = str.join("\n", htmlLines)
def action_default(self):
"""Look for related orders by association / recommender methods"""
self.recommender = ItemAssociationRecommender()
# Instance to test on
self.recommender.dataManager.dataCache = webDataCache
query = RecommenderQuery()
if self.requestData["sortField"] == "":
self.requestData["sortField"] = "P-YatesChi2-NegLog"
# P-Fisher-NegLog should yield better results, but beware, much longer to calculate
query.parseParams(self.requestData)
if len(query.excludeItemIds) == 0:
query.excludeItemIds = self.recommender.defaultExcludedClinicalItemIds(
)
if len(query.excludeCategoryIds) == 0:
query.excludeCategoryIds = self.recommender.defaultExcludedClinicalItemCategoryIds(
)
#query.fieldList.extend( ["prevalence","PPV","RR"] );
displayFields = list()
if self.requestData["displayFields"] != "":
displayFields = self.requestData["displayFields"].split(",")
recommendedData = self.recommender(query)
if len(recommendedData) > 0:
# Denormalize results with links to clinical item descriptions
self.recommender.formatRecommenderResults(recommendedData)
# Display fields should append Format suffix to identify which version to display, but use original for header labels
(self.requestData["fieldHeaders"], displayFieldsFormatSuffixed
) = self.prepareDisplayHeaders(displayFields)
# Format for HTML and add a control field for interaction with the data
for dataModel in recommendedData:
self.prepareResultRow(dataModel, displayFields)
# Try organize by category
if self.requestData["groupByCategory"]:
recommendedData = self.recommender.organizeByCategory(
recommendedData)
colNames = ["controls"]
# "name" for code. ,"category_description"
colNames.extend(displayFieldsFormatSuffixed)
colNames.extend(["description"])
lastModel = None
htmlLines = list()
for dataModel in recommendedData:
newCategory = (lastModel is None
or lastModel["category_description"] !=
dataModel["category_description"])
showCategory = (self.requestData["groupByCategory"]
and newCategory)
# Limit category display if many repeats
if showCategory:
htmlLines.append(CATEGORY_HEADER_TEMPLATE % dataModel)
htmlLines.append(
self.formatRowHTML(dataModel, colNames, showCategory))
lastModel = dataModel
self.requestData["dataRows"] = str.join("\n", htmlLines)