app.py

import streamlit as st
import pandas as pd
import numpy as np
from math import ceil
from collections import Counter
from string import punctuation
import spacy
from spacy import displacy
from spacy.lang.en import English
from spacy.matcher import PhraseMatcher
from spacy.tokens import Span
from negspacy.negation import Negex
#import en_ner_bc5cdr_md
import re


from streamlit.components.v1 import html

# Store the initial value of widgets in session state
if "visibility" not in st.session_state:
    st.session_state.visibility = "visible"
    st.session_state.disabled = False

#nlp = en_core_web_lg.load()
nlp = spacy.load("en_ner_bc5cdr_md")
nlp0 = spacy.load("en_ner_bc5cdr_md")
nlp1 = spacy.load("en_ner_bc5cdr_md")

st.set_page_config(page_title ='Clinical Note Summarization', 
                   #page_icon= "Notes",
                   layout='wide')
st.title('Clinical Note Summarization')
st.markdown(
    """
    <style>
    [data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
        width: 400px;
    }
    [data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
        width: 400px;
        margin-left: -230px;
    }
    </style>
    """,
    unsafe_allow_html=True,
)
st.sidebar.markdown('Using transformer model')

## Loading in dataset
df = pd.read_csv('shpi25nov.csv')

#Loading in Admission chief Complaint and diagnosis
df2 = pd.read_csv('cohort_cc_adm_diag.csv')

#Loading in Dischare History
df3 = pd.read_csv('cohort_past_history_12072022.csv')

#combining both data into one 
df = pd.merge(df, df2, on=['HADM_ID','SUBJECT_ID'])

df['HADM_ID'] = df['HADM_ID'].astype(str).apply(lambda x: x.replace('.0',''))
df3['HADM_ID'] = df3['HADM_ID'].astype(str).apply(lambda x: x.replace('.0',''))

del df2

#Renaming column
df.rename(columns={'SUBJECT_ID':'Patient_ID',
                  'HADM_ID':'Admission_ID',
                  'hpi_input_text':'Original_Text',
                  'hpi_reference_summary':'Reference_text'}, inplace = True)
df3.rename(columns={'SUBJECT_ID':'Patient_ID'}, inplace = True) 

 #data.rename(columns={'gdp':'log(gdp)'}, inplace=True)

#Filter selection 
st.sidebar.header("Search for Patient:")

patientid = df['Patient_ID']
patient = st.sidebar.selectbox('Select Patient ID:', patientid)
admissionid = df['Admission_ID'].loc[df['Patient_ID'] == patient]
HospitalAdmission = st.sidebar.selectbox(' ', admissionid)
pastHistoryEpid = df3['HADM_ID'].loc[df3['Patient_ID'] == patient]


# List of Model available
model = st.sidebar.selectbox('Select Model', ('BertSummarizer','BertGPT2','t5seq2eq','t5','gensim','pysummarizer'))

col3,col4 = st.columns(2) 
patientid = col3.write(f"Patient ID:  {patient} ")
admissionid =col4.write(f"Admission ID:  {HospitalAdmission} ")
runtext = ''
inputNote  ='Input note here:'
# Query out relevant Clinical notes
original_text =  df.query(
    "Patient_ID  == @patient & Admission_ID == @HospitalAdmission"
)

original_text2 = original_text['Original_Text'].values
AdmissionChiefCom = original_text['Admission_Chief_Complaint'].values
diagnosis =original_text['DIAGNOSIS'].values
reference_text = original_text['Reference_text'].values



##========= Buttons to the 5 tabs ======== Temp disabled Discharge Plan and Social Notes 
##col1, col2, col3, col4, col5 = st.columns([1,1,1,1,1]) -- to uncomment and comment below line to include discharge plan and social notes 
col1, col2, col5 = st.columns([1,1,1])
col6, col7, col8 =st.columns([2,2,2])
with st.container():
    with col1:
        btnAdmission = st.button("🏥 Admission")
        if btnAdmission:
            #nav_page('Admission')
            inputNote = "Input Admission Note"
    with col2:
        btnDailyNarrative = st.button('📆Daily Narrative')
        if btnDailyNarrative:
            inputNote = "Input Daily Narrative Note"
#    with col3:
#        btnDischargePlan = st.button('🗒️Discharge Plan')
#        if btnDischargePlan:
#            inputNote = "Input Discharge Plan"
#    with col4:
#        btnSocialNotes = st.button('📝Social Notes')
#        if btnSocialNotes: 
#            inputNote = "Input Social Note"
    with col5:
        btnPastHistory = st.button('📇Past History (6 Mths)')
        if btnPastHistory:          
            inputNote = "Input History records"

##========= on Past History Tab  =========
if btnPastHistory:
    st.text_area('Past History','Date of discharge: xxxxxxxxx')
    
else:
    runtext =st.text_area(inputNote, str(original_text2), height=300)
    
    
with st.container(): 
    if btnPastHistory:
        with col6: 
            st.markdown('**No. of admission past 6 months: xx**')
        with col7: 
            st.text_area('Discharge Disposition:',' ', height=8) #to replace with dropdown list if data is available 
        with col8:
            #st.date_input('Select Admission Date') # To replace with a dropdown filter instead 
            #st.selectbox('Past Episodes',pastHistoryEp)
            pastHistory = st.selectbox(' ', pastHistoryEpid)
    
##========= END on Past History Tab  =========


# Extract words associated with each entity
def genEntities(ann, entity):
              # entity colour dict
              #ent_col = {'DISEASE':'#B42D1B', 'CHEMICAL':'#F06292'}
              ent_col = {'DISEASE':'pink', 'CHEMICAL':'orange'}
              # separate into the different entities
              entities = trans_df['Class'].unique()

              if entity in entities:
                             ent = list(trans_df[trans_df['Class']==entity]['Entity'].unique())
                             entlist = ",".join(ent)
                             st.markdown(f'<p style="background-color:{ent_col[entity]};color:#080808;font-size:16px;">{entlist}</p>', unsafe_allow_html=True)


##======================== 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)
# ====== 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)

##======================== End of NER Tagging ========================

def run_model(input_text):    
    if model == "BertSummarizer":
        output = original_text['BertSummarizer2s'].values
        st.write('Summary')

    elif model == "BertGPT2":
        output = original_text['BertGPT2'].values
        st.write('Summary')
        
      
    elif model == "t5seq2eq": 
        output = original_text['t5seq2eq'].values
        st.write('Summary')
         
    elif model == "t5":
        output = original_text['t5'].values
        st.write('Summary')
        
    elif model == "gensim": 
        output = original_text['gensim'].values
        st.write('Summary')
        
    elif model == "pysummarizer": 
        output = original_text['pysummarizer'].values
        st.write('Summary')


    
    st.success(output)


col1, col2 = st.columns([1,1])

with col1:
    if not(btnPastHistory): #to not show summary and references text for Past History
        st.button('Summarize')
        run_model(runtext)
        #sentences=runtext.split('.')
        st.text_area('Reference text', str(reference_text), height=150)
    else: 
        with st.expander('Full Discharge Summary'):
            st.write( str(original_text2))
    ##====== Storing the Diseases/Text
    table= {"Entity":[], "Class":[]}
    ent_bc = {}
    for x in doc.ents:
        ent_bc[x.text] = x.label_
    for key in ent_bc:
        table["Entity"].append(key)
        table["Class"].append(ent_bc[key])
    trans_df = pd.DataFrame(table)

with col2:
    if st.button('NER'):
        st.markdown('**CHIEF COMPLAINT:**')
        st.write(str(AdmissionChiefCom))
        st.markdown('**ADMISSION DIAGNOSIS:**')
        st.markdown(str(diagnosis))
        st.markdown('**PROBLEM/ISSUE**')
        genEntities(trans_df, 'DISEASE')
        st.markdown('**MEDICATION**')
        genEntities(trans_df, 'CHEMICAL')
        #st.table(trans_df)   
        st.markdown('**NER**')
        with st.expander("See NER Details"):
            st.markdown(ent_html, unsafe_allow_html=True)