Gradient Boosting (LightGBM) - Calibrated for Hospital Readmission Prediction

Model Description

This is a calibrated Gradient Boosting (LightGBM) model for predicting 30-day hospital readmission risk in diabetic patients. The model has been calibrated using PLATT to ensure that predicted probabilities accurately reflect true readmission risk.

Calibration Method: PLATT
Calibration Date: 2025-12-10 08:15:54
Base Model: Gradient Boosting (LightGBM)

Why Calibration Matters

Model calibration ensures that predicted probabilities are reliable for clinical decision-making:

A predicted 15% risk should mean ~15 out of 100 similar patients are readmitted
Enables accurate risk stratification and resource allocation
Critical for patient safety and clinical trust
Required for regulatory compliance in healthcare AI

Calibration Performance

Success Criteria

Criterion	Target	Result	Status
Brier Score	< 0.15	0.0818	✅ PASS
ECE (±5% accuracy)	< 0.05	0.0180	✅ PASS
Hosmer-Lemeshow Test	p > 0.05	0.0000	❌ FAIL

Overall Result: ⚠️ SOME CRITERIA NOT MET

Before vs After Calibration

Metric	Uncalibrated	Calibrated	Improvement
Brier Score	0.0802	0.0818	-0.0016
Log Loss	0.2665	0.2734	-0.0068
ECE	0.0064	0.0180	-0.0116
ROC-AUC	0.8424	0.8424	Unchanged*

*Note: Calibration improves probability estimates without changing discrimination (ranking) ability.

Hosmer-Lemeshow Goodness-of-Fit Test

Uncalibrated: χ²=15.23, p=0.0185 (Poorly calibrated)
Calibrated: χ²=136.54, p=0.0000 (Poorly calibrated)

Clinical Risk Categories

Calibrated probabilities are mapped to actionable risk categories:

Risk Level	Probability Range	Recommended Action
Low	0-5%	Standard discharge planning
Medium	5-15%	Enhanced patient education + 1-week follow-up call
High	15%+	Intensive case management + home health visit

Risk Category Validation

The table below shows how well predicted risk categories align with actual readmission rates:

Visualizations

Reliability Diagram (Calibration Curve)

Shows how well predicted probabilities match observed frequencies. The closer to the diagonal, the better calibrated.

Reliability Comparison (Before vs After)

Compares uncalibrated vs calibrated predictions.

Risk Distribution

Distribution of patients across risk categories.

Detailed Risk Distribution

Enhanced visualization with probability thresholds.

Usage

Loading the Calibrated Model

import joblib
import pandas as pd
import sys

# Add utilities to path
sys.path.append('./phase-3-model-calibration')
from utilities import ModelCalibrator

# Load original model
model = joblib.load('gradient_boosting_model_original.joblib')

# Load calibrator
calibrator = ModelCalibrator.load('Gradient_Boosting_(LightGBM)_calibrator.pkl')

# Load your preprocessed features (MUST use same preprocessing as training!)
X_new = pd.read_csv('your_preprocessed_features.csv')

# Step 1: Get uncalibrated predictions from original model
uncalibrated_proba = model.predict_proba(X_new)[:, 1]

# Step 2: Apply calibration
calibrated_proba = calibrator.predict_proba(uncalibrated_proba)

# Create results DataFrame
results = pd.DataFrame({
    'patient_id': X_new.index,
    'uncalibrated_probability': uncalibrated_proba,
    'calibrated_probability': calibrated_proba
})

# Display results
print(results.head(10))

# Example output:
#    patient_id  uncalibrated_probability  calibrated_probability risk_category  recommended_action
# 0           0                    0.0834                  0.0234           Low  Standard discharge
# 1           1                    0.2341                  0.1876          High  Intensive case management, home visit
# 2           2                    0.1123                  0.0891        Medium  Enhanced education, 1-week follow-up

Quick Prediction Pipeline

def predict_readmission_risk(patient_features):
    """
    Complete pipeline for readmission risk prediction.
    
    Args:
        patient_features: DataFrame with preprocessed patient features
        
    Returns:
        DataFrame with calibrated probabilities
    """
    # Load models
    model = joblib.load('gradient_boosting_model_original.joblib')
    calibrator = ModelCalibrator.load('Gradient_Boosting_(LightGBM)_calibrator.pkl')
    
    # Generate calibrated predictions
    uncalibrated = model.predict_proba(patient_features)[:, 1]
    calibrated = calibrator.predict_proba(uncalibrated)
    
    # Return calibrated probabilities
    results = pd.DataFrame({
        'readmission_probability': calibrated
    })
    
    return results

# Use the pipeline
predictions = predict_readmission_risk(X_new)
print(predictions)

Important Notes

1. Preprocessing Requirements

Input features MUST be preprocessed using the exact same pipeline as training:

Same missing value imputation
Same feature engineering
Same scaling/encoding
Same feature set

See phase-1-data-explore-preprocessing/simple_preprocessing.py for the preprocessing pipeline.

2. Calibration Preserves Discrimination

Calibration improves probability estimates
Does NOT change model's ranking ability (ROC-AUC stays the same)
Patients ranked as higher risk remain higher risk

3. When to Recalibrate

Recalibrate the model when:

Patient population characteristics change
Healthcare practices evolve
Model performance degrades
Annually as a best practice

4. Clinical Validation Required

Before deployment:

Validate risk thresholds with clinical experts
Test on local patient population
Ensure alignment with clinical workflows
Obtain necessary regulatory approvals

Files Included

gradient_boosting_model_original.joblib - Original trained model
Gradient_Boosting_(LightGBM)_calibrator.pkl - Calibration transformer (platt)
Gradient_Boosting_(LightGBM)_report.txt - Detailed calibration report
Gradient_Boosting_(LightGBM)_metrics.json - Metrics in JSON format
calibration_comparison_metrics.json - Before/after comparison
risk_validation_detailed.csv - Risk category validation table
reliability_diagram*.png - Calibration visualizations
risk_distribution*.png - Risk distribution plots
DEPLOYMENT_INSTRUCTIONS.md - Deployment guide

Limitations and Ethical Considerations

Limitations

Domain-Specific: Trained for diabetic patient readmissions only
Temporal Drift: Data from 1999-2008 may not reflect current practices
Geographic Bias: US hospital data may not generalize internationally
Population Shift: Recalibration needed if patient demographics change

Ethical Considerations

This model should:

✅ Assist clinical decision-making, not replace it
✅ Be validated on your local patient population
✅ Be monitored for fairness across demographic groups
✅ Be recalibrated regularly with recent data
❌ NOT be the sole basis for treatment decisions
❌ NOT be deployed without clinical expert validation

Fairness

Evaluate calibration quality separately for different demographic groups
Monitor for disparate impact across protected attributes
Consider group-specific calibration if needed
Document fairness metrics for regulatory compliance

Citation

@misc{hospital-readmission-calibrated,
  title={Calibrated Model for Hospital Readmission Prediction},
  author={Your Name},
  year={2025},
  howpublished={\url{https://huggingface.co/your-username/your-repo}}
}

Dataset Citation

@article{strack2014impact,
  title={Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records},
  author={Strack, Beata and DeShazo, Jonathan P and Gennings, Chris and Olmo, Juan L and Ventura, Sebastian and Cios, Krzysztof J and Clore, John N},
  journal={BioMed Research International},
  volume={2014},
  year={2014},
  publisher={Hindawi}
}

License

This calibrated model is released under the MIT License. The underlying dataset and original model have their own license terms.

Contact

For questions or issues, please open an issue in the repository.

Disclaimer: This model is for research and educational purposes. Always consult healthcare professionals for medical decisions. Regular monitoring and recalibration are essential for safe deployment.

Last Updated: 2025-12-10 08:15:54

Downloads last month: -; Downloads are not tracked for this model. How to track