Extract hemograms concepts

Hemograms/BertHemograms.py

+import json
+import copy
+import datetime
+from datetime import date
+import nltk
+import requests
+import pandas as pd
+from ConceptExtractor_v3 import extractionOfConcepts
+from timeit import default_timer
+currentDate = str(date.today()).split(" ")[0]
+
+def dictConceptsDoc(listTuples):
+    dict = {}
+    listConcepts = []
+    for i in range(len(listTuples)):
+        listConcepts.clear()
+        idDoc = listTuples[i][4]
+        if idDoc not in dict:
+            for j in range(len(listTuples)):
+                if idDoc == listTuples[j][4]:
+                    listConcepts.append(listTuples[j])
+            dict[idDoc] = copy.copy(listConcepts)
+    return dict
+
+def checkDoses(idDoc, sentDocs):
+    countSents = 0
+    metricsList = []
+    dictMetrics = {}
+    resultList = []
+    for sents in sentDocs:
+        resp = requests.post(url="http://138.4.130.153:8088/jkes/annotator/dateAnnotator",
+                             json={idDoc: [currentDate, sents]},
+                             verify=False)
+        result_metrics = resp.json()
+        metricsList.append(result_metrics['response'][1])
+        #if dictMetrics != {}:
+        #    metricsList.append([dictMetrics[idDoc]])
+        countSents += 1
+    for j in range(len(metricsList)):
+        for key in metricsList[j]:
+            if metricsList[j][key] != []:
+                resultList.append(metricsList[j][key])
+    dictMetrics[idDoc] = copy.copy(resultList)
+    return dictMetrics
+
+def bertConcepts():
+    inicio = default_timer()
+    fileDocs = 'breast_notes_100randomly_sampled.xlsx'
+    textDocs = pd.read_excel(fileDocs, sheet_name='Sheet1')
+    textDocs = textDocs['DESCRIPCION']
+    sentsDoc = {}
+    spanish_tokenizer = nltk.data.load('tokenizers/punkt/spanish.pickle')
+    for i in range(len(textDocs)):
+        sentsDoc[i] = spanish_tokenizer.tokenize(textDocs[i])
+    listHemogramConcepts = []
+    dictAnotacionesDoc = {}
+    dictMetrics = {}
+    with open('annotations.json') as json_file:
+        annotations = json.load(json_file)
+    data = extractionOfConcepts(annotations)
+    with open('entity_cuis.json') as file:
+        dictAnnotationsEntities = json.load(file)
+    resultado = extractionOfConcepts(annotations)
+    anotaciones = [anotacion for lista in resultado for anotacion in lista]
+    dictAnotacionesDoc = dictConceptsDoc(anotaciones)
+    #anotaciones = [concept, Entity, offset, end, ID]
+    for key in dictAnotacionesDoc.keys():
+        for tupleAnot in dictAnotacionesDoc[key]:
+            if tupleAnot[1] == 'DATE':
+                idDoc = tupleAnot[4]
+                listHemogramConcepts.append(tupleAnot)
+                dictMetrics = checkDoses(idDoc, sentsDoc[idDoc])
+                for key in dictMetrics:
+                    for metrics in dictMetrics[key]:
+                        for metricConcept in (metrics):
+                            for z in range(len(metricConcept)):
+                                if metricConcept[z][5] != 'NUMBER':
+                                    distanceMetric = sentsDoc[idDoc].find(metrics[0])
+                                    distanceConcept = sentsDoc[idDoc].find(tupleAnot[0])
+                                    if(distanceMetric - distanceConcept < 2):
+                                        completeHemogram = tupleAnot[0] + metrics[0]
+                                        print(completeHemogram)
+                print("wait")
+bertConcepts()
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