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import os |
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import logging |
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import pickle |
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from typing import Dict, Any, Union, Optional |
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import numpy as np |
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import joblib |
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logger = logging.getLogger(__name__) |
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class SimpleReadmissionModel: |
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""" |
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A simple model for predicting hospital readmission risk |
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Can be replaced with a more sophisticated ML model |
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""" |
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def __init__(self): |
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"""Initialize the readmission risk model""" |
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self.feature_weights = { |
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'age': 0.02, |
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'num_conditions': 0.15, |
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'num_medications': 0.1 |
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} |
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def predict(self, features: Dict[str, Any]) -> float: |
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""" |
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Predict readmission risk based on input features |
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:param features: Dictionary of input features |
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:return: Predicted readmission risk score |
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""" |
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risk_score = 0.0 |
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for feature, weight in self.feature_weights.items(): |
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risk_score += features.get(feature, 0) * weight |
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return risk_score |
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def load_model(model_path="model.joblib"): |
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""" |
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Load a pre-trained model from disk (Joblib, Pickle, or any format). |
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For hackathon demonstration, you can store a simple logistic regression or XGBoost model. |
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""" |
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try: |
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model = joblib.load(model_path) |
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return model |
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except: |
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print("Warning: No real model found. Using mock predictions.") |
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return None |
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def predict_readmission_risk(model, patient_data: dict) -> float: |
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""" |
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Given patient_data (dict) and a loaded model, return a risk score [0,1]. |
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If model is None, return a random or fixed value for demonstration. |
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""" |
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if model is None: |
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return 0.8 |
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else: |
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age = patient_data.get('age', 50) |
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num_conditions = patient_data.get('num_conditions', 2) |
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num_medications = patient_data.get('num_medications', 5) |
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X = np.array([[age, num_conditions, num_medications]]) |
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prob = model.predict_proba(X)[0,1] |
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return float(prob) |
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