Update app.py

app.py

@@ -166,97 +166,97 @@ def genEntities(ann, entity):
 
 ##======================== Start of NER Tagging ========================
 # ====== Old NER ======
-#doc = nlp(str(original_text2))
-#colors = { "DISEASE": "pink","CHEMICAL": "orange"}
-#options = {"ents": [ "DISEASE", "CHEMICAL"],"colors": colors}
-#ent_html = displacy.render(doc, style="ent", options=options)
+doc = nlp(str(original_text2))
+colors = { "DISEASE": "pink","CHEMICAL": "orange"}
+options = {"ents": [ "DISEASE", "CHEMICAL"],"colors": colors}
+ent_html = displacy.render(doc, style="ent", options=options)
 # ====== End of Old NER ======
                   
-#lemmatizing the notes to capture all forms of negation(e.g., deny: denies, denying)
-def lemmatize(note, nlp):
-    doc = nlp(note)
-    lemNote = [wd.lemma_ for wd in doc]
-    return " ".join(lemNote)
-
-#function to modify options for displacy NER visualization
-def get_entity_options():
-    entities = ["DISEASE", "CHEMICAL", "NEG_ENTITY"]
-    colors = {'DISEASE': 'pink', 'CHEMICAL': 'orange', "NEG_ENTITY":'white'}
-    options = {"ents": entities, "colors": colors}    
-    return options
-
-#adding a new pipeline component to identify negation
-def neg_model(nlp_model):
-    nlp = spacy.load(nlp_model, disable = ['parser'])
-#     nlp.add_pipe(nlp.create_pipe('sentencizer'))
-    nlp.add_pipe('sentencizer')
-#     negex = Negex(nlp)
-    nlp.add_pipe(
-    "negex",
-    config={
-        "chunk_prefix": ["no"],
-    },
-    last=True)
-    return nlp
-
-def negation_handling(nlp_model, note, neg_model):
-    results = []
-    nlp = neg_model(nlp_model) 
-    note = note.split(".") #sentence tokenizing based on delimeter 
-    note = [n.strip() for n in note] #removing extra spaces at the begining and end of sentence
-    for t in note:
-        doc = nlp(t)
-        for e in doc.ents:
-            rs = str(e._.negex)
-            if rs == "True": 
-                results.append(e.text)
-    return results
-
-#function to identify span objects of matched negative phrases from text
-def match(nlp,terms,label):
-    patterns = [nlp.make_doc(text) for text in terms]
-    matcher = PhraseMatcher(nlp.vocab)
-    matcher.add(label, None, *patterns)
-    return matcher
-
-#replacing the labels for identified negative entities
-def overwrite_ent_lbl(matcher, doc):
-    matches = matcher(doc)
-    seen_tokens = set()
-    new_entities = []
-    entities = doc.ents
-    for match_id, start, end in matches:
-        if start not in seen_tokens and end - 1 not in seen_tokens:
-            new_entities.append(Span(doc, start, end, label=match_id))
-            entities = [e for e in entities if not (e.start < end and e.end > start)]
-            seen_tokens.update(range(start, end))
-    doc.ents = tuple(entities) + tuple(new_entities)
-    return doc
-
-#deduplicate repeated entities 
-def dedupe(items):
-    seen = set()
-    for item in items:
-        item = str(item).strip()
-        if item not in seen:
-            yield item
-            seen.add(item)
-
-lem_clinical_note= lemmatize(runtext, nlp0)
-#creating a doc object using BC5CDR model
-doc = nlp1(lem_clinical_note)
-options = get_entity_options()
-
-#list of negative concepts from clinical note identified by negspacy
-results0 = negation_handling("en_ner_bc5cdr_md", lem_clinical_note, neg_model)
-
-matcher = match(nlp1, results0,"NEG_ENTITY")
-
-#doc0: new doc object with added "NEG_ENTITY label"
-doc0 = overwrite_ent_lbl(matcher,doc)
-
-#visualizing identified Named Entities in clinical input text 
-ent_html = displacy.render(doc0, style='ent', options=options)
+# #lemmatizing the notes to capture all forms of negation(e.g., deny: denies, denying)
+# def lemmatize(note, nlp):
+    # doc = nlp(note)
+    # lemNote = [wd.lemma_ for wd in doc]
+    # return " ".join(lemNote)
+
+# #function to modify options for displacy NER visualization
+# def get_entity_options():
+    # entities = ["DISEASE", "CHEMICAL", "NEG_ENTITY"]
+    # colors = {'DISEASE': 'pink', 'CHEMICAL': 'orange', "NEG_ENTITY":'white'}
+    # options = {"ents": entities, "colors": colors}    
+    # return options
+
+# #adding a new pipeline component to identify negation
+# def neg_model(nlp_model):
+    # nlp = spacy.load(nlp_model, disable = ['parser'])
+# #     nlp.add_pipe(nlp.create_pipe('sentencizer'))
+    # nlp.add_pipe('sentencizer')
+# #     negex = Negex(nlp)
+    # nlp.add_pipe(
+    # "negex",
+    # config={
+        # "chunk_prefix": ["no"],
+    # },
+    # last=True)
+    # return nlp
+
+# def negation_handling(nlp_model, note, neg_model):
+    # results = []
+    # nlp = neg_model(nlp_model) 
+    # note = note.split(".") #sentence tokenizing based on delimeter 
+    # note = [n.strip() for n in note] #removing extra spaces at the begining and end of sentence
+    # for t in note:
+        # doc = nlp(t)
+        # for e in doc.ents:
+            # rs = str(e._.negex)
+            # if rs == "True": 
+                # results.append(e.text)
+    # return results
+
+# #function to identify span objects of matched negative phrases from text
+# def match(nlp,terms,label):
+    # patterns = [nlp.make_doc(text) for text in terms]
+    # matcher = PhraseMatcher(nlp.vocab)
+    # matcher.add(label, None, *patterns)
+    # return matcher
+
+# #replacing the labels for identified negative entities
+# def overwrite_ent_lbl(matcher, doc):
+    # matches = matcher(doc)
+    # seen_tokens = set()
+    # new_entities = []
+    # entities = doc.ents
+    # for match_id, start, end in matches:
+        # if start not in seen_tokens and end - 1 not in seen_tokens:
+            # new_entities.append(Span(doc, start, end, label=match_id))
+            # entities = [e for e in entities if not (e.start < end and e.end > start)]
+            # seen_tokens.update(range(start, end))
+    # doc.ents = tuple(entities) + tuple(new_entities)
+    # return doc
+
+# #deduplicate repeated entities 
+# def dedupe(items):
+    # seen = set()
+    # for item in items:
+        # item = str(item).strip()
+        # if item not in seen:
+            # yield item
+            # seen.add(item)
+
+# lem_clinical_note= lemmatize(runtext, nlp0)
+# #creating a doc object using BC5CDR model
+# doc = nlp1(lem_clinical_note)
+# options = get_entity_options()
+
+# #list of negative concepts from clinical note identified by negspacy
+# results0 = negation_handling("en_ner_bc5cdr_md", lem_clinical_note, neg_model)
+
+# matcher = match(nlp1, results0,"NEG_ENTITY")
+
+# #doc0: new doc object with added "NEG_ENTITY label"
+# doc0 = overwrite_ent_lbl(matcher,doc)
+
+# #visualizing identified Named Entities in clinical input text 
+# ent_html = displacy.render(doc0, style='ent', options=options)
 
 ##======================== End of NER Tagging ========================