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| import torch | |
| import json | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from sklearn.preprocessing import MultiLabelBinarizer | |
| import numpy as np | |
| import re | |
| # ---------------------------------------------------------------------- | |
| # Text Preprocessing (same as during training) | |
| # ---------------------------------------------------------------------- | |
| def preprocess_text(text: str) -> str: | |
| """ | |
| Perform advanced text cleaning: | |
| - Convert to lowercase | |
| - Remove bracketed deidentifications [**...**] | |
| - Remove excessive punctuation | |
| - Convert multiple spaces/newlines to single space | |
| - Strip whitespace | |
| """ | |
| text = text.lower() | |
| text = re.sub(r"\[\*\*.*?\*\*\]", " ", text) # remove deidentified brackets | |
| text = re.sub(r"([!?.,])\1+", r"\1", text) # collapse repeated punctuation | |
| text = re.sub(r"[\r\n\t]+", " ", text) # collapse lines/tabs to space | |
| text = re.sub(r"\s+", " ", text) # multiple spaces -> single | |
| text = text.strip() | |
| return text | |
| # ---------------------------------------------------------------------- | |
| # Load Trained Model and Artifacts | |
| # ---------------------------------------------------------------------- | |
| def load_trained_model(model_dir: str): | |
| """ | |
| Load the trained model, tokenizer, and MultiLabelBinarizer. | |
| """ | |
| # Load the model | |
| model = AutoModelForSequenceClassification.from_pretrained(model_dir) | |
| model.eval() # Set to evaluation mode | |
| # Load the tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(model_dir) | |
| # Load the MultiLabelBinarizer | |
| with open(f"{model_dir}/mlb_classes.json", "r") as f: | |
| top_codes_list = json.load(f) | |
| mlb = MultiLabelBinarizer(classes=top_codes_list) | |
| mlb.fit([[]]) # Initialize the binarizer | |
| return model, tokenizer, mlb | |
| # ---------------------------------------------------------------------- | |
| # Predict ICD-9 Codes | |
| # ---------------------------------------------------------------------- | |
| def predict_icd9(input_text: str, model, tokenizer, mlb, max_length=512, threshold=0.5): | |
| """ | |
| Predict ICD-9 codes for a given clinical text. | |
| """ | |
| # Preprocess the input text | |
| processed_text = preprocess_text(input_text) | |
| # Tokenize the input | |
| inputs = tokenizer( | |
| processed_text, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=max_length, | |
| padding="max_length" | |
| ) | |
| # Perform inference | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| # Apply sigmoid to convert logits to probabilities | |
| probs = torch.sigmoid(logits).squeeze().cpu().numpy() | |
| # Apply threshold to get predicted labels | |
| y_pred = (probs > threshold).astype(int) | |
| # Decode the predicted labels back to ICD-9 codes | |
| predicted_codes = mlb.inverse_transform(np.array([y_pred])) # Ensure 2D array | |
| return predicted_codes[0] # Return as a list of codes | |
| # ---------------------------------------------------------------------- | |
| # Inference Example | |
| # ---------------------------------------------------------------------- | |
| if __name__ == "__main__": | |
| # Directory where the model and artifacts are saved | |
| model_dir = "./" | |
| # Load the model and related artifacts | |
| model, tokenizer, mlb = load_trained_model(model_dir) | |
| # Example input text | |
| input_text = """Acute nasopharyngitis , true acute abnormality """ | |
| # Predict ICD-9 codes | |
| predicted_codes = predict_icd9(input_text, model, tokenizer, mlb, threshold= 0.2) | |
| # Print the predicted ICD-9 codes | |
| print("Predicted ICD-9 Codes:", predicted_codes) | |