Spaces:
Running
Running
File size: 5,513 Bytes
a297be3 90f10bf a297be3 90f10bf a297be3 90f10bf a297be3 90f10bf a297be3 90f10bf a297be3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
# 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.")
|