tonic-discharge-guard / predictive_model.py
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Add pdfgenerator, readmission model
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import os
import logging
import pickle
from typing import Dict, Any, Union, Optional
import numpy as np
import joblib
logger = logging.getLogger(__name__)
class SimpleReadmissionModel:
"""
A simple model for predicting hospital readmission risk
Can be replaced with a more sophisticated ML model
"""
def __init__(self):
"""Initialize the readmission risk model"""
self.feature_weights = {
'age': 0.02, # Higher age increases risk slightly
'num_conditions': 0.15, # More conditions increase risk
'num_medications': 0.1 # More medications increase risk
}
def predict(self, features: Dict[str, Any]) -> float:
"""
Predict readmission risk based on input features
:param features: Dictionary of input features
:return: Predicted readmission risk score
"""
risk_score = 0.0
for feature, weight in self.feature_weights.items():
risk_score += features.get(feature, 0) * weight
return risk_score
def load_model(model_path="model.joblib"):
"""
Load a pre-trained model from disk (Joblib, Pickle, or any format).
For hackathon demonstration, you can store a simple logistic regression or XGBoost model.
"""
# For now, let's assume you've already trained a model and saved it as model.joblib
# If you don't have a real model, you could mock or return None.
try:
model = joblib.load(model_path)
return model
except:
# If no real model is available, just return None or a dummy object
print("Warning: No real model found. Using mock predictions.")
return None
def predict_readmission_risk(model, patient_data: dict) -> float:
"""
Given patient_data (dict) and a loaded model, return a risk score [0,1].
If model is None, return a random or fixed value for demonstration.
"""
if model is None:
# Mock for demonstration
return 0.8 # always return 80% risk
else:
# Example feature extraction
# Suppose your model expects [age, num_conditions, num_medications]
age = patient_data.get('age', 50)
num_conditions = patient_data.get('num_conditions', 2)
num_medications = patient_data.get('num_medications', 5)
X = np.array([[age, num_conditions, num_medications]])
# If it's a classifier with predict_proba
prob = model.predict_proba(X)[0,1]
return float(prob)