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# import streamlit as st
# import torch
# from transformers import LongformerTokenizer, LongformerForSequenceClassification
# # Load the fine-tuned model and tokenizer
# model_path = "./clinical_longformer"
# tokenizer = LongformerTokenizer.from_pretrained(model_path)
# model = LongformerForSequenceClassification.from_pretrained(model_path)
# model.eval() # Set the model to evaluation mode
# # ICD-9 code columns used during training
# icd9_columns = [
# '038.9', '244.9', '250.00', '272.0', '272.4', '276.1', '276.2', '285.1', '285.9',
# '287.5', '305.1', '311', '36.15', '37.22', '37.23', '38.91', '38.93', '39.61',
# '39.95', '401.9', '403.90', '410.71', '412', '414.01', '424.0', '427.31', '428.0',
# '486', '496', '507.0', '511.9', '518.81', '530.81', '584.9', '585.9', '599.0',
# '88.56', '88.72', '93.90', '96.04', '96.6', '96.71', '96.72', '99.04', '99.15',
# '995.92', 'V15.82', 'V45.81', 'V45.82', 'V58.61'
# ]
# # Function for making predictions
# def predict_icd9(texts, tokenizer, model, threshold=0.5):
# inputs = tokenizer(
# texts,
# padding="max_length",
# truncation=True,
# max_length=512,
# return_tensors="pt"
# )
# with torch.no_grad():
# outputs = model(
# input_ids=inputs["input_ids"],
# attention_mask=inputs["attention_mask"]
# )
# logits = outputs.logits
# probabilities = torch.sigmoid(logits)
# predictions = (probabilities > threshold).int()
# predicted_icd9 = []
# for pred in predictions:
# codes = [icd9_columns[i] for i, val in enumerate(pred) if val == 1]
# predicted_icd9.append(codes)
# return predicted_icd9
# # Streamlit UI
# st.title("ICD-9 Code Prediction")
# st.sidebar.header("Model Options")
# model_option = st.sidebar.selectbox("Select Model", [ "ClinicalLongformer"])
# threshold = st.sidebar.slider("Prediction Threshold", 0.0, 1.0, 0.5, 0.01)
# st.write("### Enter Medical Summary")
# input_text = st.text_area("Medical Summary", placeholder="Enter clinical notes here...")
# if st.button("Predict"):
# if input_text.strip():
# predictions = predict_icd9([input_text], tokenizer, model, threshold)
# st.write("### Predicted ICD-9 Codes")
# for code in predictions[0]:
# st.write(f"- {code}")
# else:
# st.error("Please enter a medical summary.")
# import torch
# import pandas as pd
# import streamlit as st
# from transformers import LongformerTokenizer, LongformerForSequenceClassification
# # Load the fine-tuned model and tokenizer
# model_path = "./clinical_longformer"
# tokenizer = LongformerTokenizer.from_pretrained(model_path)
# model = LongformerForSequenceClassification.from_pretrained(model_path)
# model.eval() # Set the model to evaluation mode
# # Load the ICD-9 descriptions from CSV into a dictionary
# icd9_desc_df = pd.read_csv("D_ICD_DIAGNOSES.csv") # Adjust the path to your CSV file
# icd9_desc_df['ICD9_CODE'] = icd9_desc_df['ICD9_CODE'].astype(str) # Ensure ICD9_CODE is string type for matching
# icd9_descriptions = dict(zip(icd9_desc_df['ICD9_CODE'].str.replace('.', ''), icd9_desc_df['LONG_TITLE'])) # Remove decimals in ICD9 code for matching
# # ICD-9 code columns used during training
# icd9_columns = [
# '038.9', '244.9', '250.00', '272.0', '272.4', '276.1', '276.2', '285.1', '285.9',
# '287.5', '305.1', '311', '36.15', '37.22', '37.23', '38.91', '38.93', '39.61',
# '39.95', '401.9', '403.90', '410.71', '412', '414.01', '424.0', '427.31', '428.0',
# '486', '496', '507.0', '511.9', '518.81', '530.81', '584.9', '585.9', '599.0',
# '88.56', '88.72', '93.90', '96.04', '96.6', '96.71', '96.72', '99.04', '99.15',
# '995.92', 'V15.82', 'V45.81', 'V45.82', 'V58.61'
# ]
# # Function for making predictions
# def predict_icd9(texts, tokenizer, model, threshold=0.5):
# inputs = tokenizer(
# texts,
# padding="max_length",
# truncation=True,
# max_length=512,
# return_tensors="pt"
# )
# with torch.no_grad():
# outputs = model(
# input_ids=inputs["input_ids"],
# attention_mask=inputs["attention_mask"]
# )
# logits = outputs.logits
# probabilities = torch.sigmoid(logits)
# predictions = (probabilities > threshold).int()
# predicted_icd9 = []
# for pred in predictions:
# codes = [icd9_columns[i] for i, val in enumerate(pred) if val == 1]
# predicted_icd9.append(codes)
# # Fetch descriptions for the predicted ICD-9 codes from the pre-loaded descriptions
# predictions_with_desc = []
# for codes in predicted_icd9:
# code_with_desc = [(code, icd9_descriptions.get(code.replace('.', ''), "Description not found")) for code in codes]
# predictions_with_desc.append(code_with_desc)
# return predictions_with_desc
# # Streamlit UI
# st.title("ICD-9 Code Prediction")
# st.sidebar.header("Model Options")
# threshold = st.sidebar.slider("Prediction Threshold", 0.0, 1.0, 0.5, 0.01)
# st.write("### Enter Medical Summary")
# input_text = st.text_area("Medical Summary", placeholder="Enter clinical notes here...")
# if st.button("Predict"):
# if input_text.strip():
# predictions = predict_icd9([input_text], tokenizer, model, threshold)
# st.write("### Predicted ICD-9 Codes and Descriptions")
# for code, description in predictions[0]:
# st.write(f"- {code}: {description}")
# else:
# st.error("Please enter a medical summary.")
import torch
import pandas as pd
import streamlit as st
from transformers import LongformerTokenizer, LongformerForSequenceClassification
# Load the fine-tuned model and tokenizer
model_path = "./clinical_longformer"
tokenizer = LongformerTokenizer.from_pretrained(model_path)
model = LongformerForSequenceClassification.from_pretrained(model_path)
model.eval() # Set the model to evaluation mode
# Load the ICD-9 descriptions from CSV into a dictionary
icd9_desc_df = pd.read_csv("D_ICD_DIAGNOSES.csv") # Adjust the path to your CSV file
icd9_desc_df['ICD9_CODE'] = icd9_desc_df['ICD9_CODE'].astype(str) # Ensure ICD9_CODE is string type
icd9_descriptions = dict(zip(icd9_desc_df['ICD9_CODE'].str.replace('.', ''), icd9_desc_df['LONG_TITLE'])) # Remove decimals for matching
# Load the ICD-9 to ICD-10 mapping
icd9_to_icd10 = {}
with open("2015_I9gem.txt", "r") as file:
for line in file:
parts = line.strip().split()
if len(parts) == 3:
icd9, icd10, _ = parts
icd9_to_icd10[icd9] = icd10
# ICD-9 code columns used during training
icd9_columns = [
'038.9', '244.9', '250.00', '272.0', '272.4', '276.1', '276.2', '285.1', '285.9',
'287.5', '305.1', '311', '36.15', '37.22', '37.23', '38.91', '38.93', '39.61',
'39.95', '401.9', '403.90', '410.71', '412', '414.01', '424.0', '427.31', '428.0',
'486', '496', '507.0', '511.9', '518.81', '530.81', '584.9', '585.9', '599.0',
'88.56', '88.72', '93.90', '96.04', '96.6', '96.71', '96.72', '99.04', '99.15',
'995.92', 'V15.82', 'V45.81', 'V45.82', 'V58.61'
]
# Function for making predictions and mapping to ICD-10
def predict_icd9(texts, tokenizer, model, threshold=0.5):
inputs = tokenizer(
texts,
padding="max_length",
truncation=True,
max_length=512,
return_tensors="pt"
)
with torch.no_grad():
outputs = model(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"]
)
logits = outputs.logits
probabilities = torch.sigmoid(logits)
predictions = (probabilities > threshold).int()
predicted_icd9 = []
for pred in predictions:
codes = [icd9_columns[i] for i, val in enumerate(pred) if val == 1]
predicted_icd9.append(codes)
# Fetch descriptions and map to ICD-10 codes
predictions_with_desc = []
for codes in predicted_icd9:
code_with_desc = []
for code in codes:
icd9_stripped = code.replace('.', '')
icd10_code = icd9_to_icd10.get(icd9_stripped, "Mapping not found")
icd9_desc = icd9_descriptions.get(icd9_stripped, "Description not found")
code_with_desc.append((code, icd9_desc, icd10_code))
predictions_with_desc.append(code_with_desc)
return predictions_with_desc
# Streamlit UI
st.title("ICD-9 to ICD-10 Code Prediction")
st.sidebar.header("Model Options")
threshold = st.sidebar.slider("Prediction Threshold", 0.0, 1.0, 0.5, 0.01)
st.write("### Enter Medical Summary")
input_text = st.text_area("Medical Summary", placeholder="Enter clinical notes here...")
if st.button("Predict"):
if input_text.strip():
predictions = predict_icd9([input_text], tokenizer, model, threshold)
st.write("### Predicted ICD-9 and ICD-10 Codes with Descriptions")
for icd9_code, description, icd10_code in predictions[0]:
st.write(f"- ICD-9: {icd9_code} ({description}) -> ICD-10: {icd10_code}")
else:
st.error("Please enter a medical summary.")
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