predict_readmission.py

import numpy as np
import tensorflow as tf

def preprocess_data(X_raw):
    """
    Preprocess the raw data for model training.
    
    Parameters:
        X_raw (array): The raw feature set.
        
    Returns:
        array: Preprocessed feature set.
    """
    # For simplicity, let's fill missing values with column means
    X_filled = np.nan_to_num(X_raw, nan=np.mean(X_raw, axis=0))
    
    # Normalize the data (min-max scaling)
    X_min = np.min(X_filled, axis=0)
    X_max = np.max(X_filled, axis=0)
    X_normalized = (X_filled - X_min) / (X_max - X_min)
    
    return X_normalized

def predict_readmission(X_train, y_train, X_test):
    """
    Build and train a model to predict patient readmission within 30 days post-discharge.
    
    Parameters:
        X_train (array): Training feature set.
        y_train (array): Training labels.
        X_test (array): Testing feature set.
        
    Returns:
        array: Predicted readmission probabilities.
    """
    # Build the model architecture
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    
    # Compile the model
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
    # Train the model
    model.fit(X_train, y_train, epochs=10)
    
    # Make predictions on the test set
    return model.predict(X_test)

# Main function to run test cases
if __name__ == "__main__":
    # Generate some synthetic training and test data
    X_train_raw = np.random.rand(100, 5)  # 100 samples, 5 features
    y_train = np.random.randint(2, size=100)  # 100 labels (0 or 1)
    X_test_raw = np.random.rand(20, 5)  # 20 test samples, 5 features
    
    # Preprocess the data
    X_train = preprocess_data(X_train_raw)
    X_test = preprocess_data(X_test_raw)
    
    # Train the model and get predictions
    predictions = predict_readmission(X_train, y_train, X_test)
    
    # Output the predictions
    print("Predicted readmission probabilities:")
    print(predictions)