IsaacT1992/hospital-readmission-lightgbm

🏥 Hospital Readmission Prediction (LightGBM)

Author: Isaac Tosin Adisa

📌 Overview

This model predicts 30-day hospital readmission risk using structured clinical features derived from the MIMIC-IV dataset. It is part of an integrated multi-model comparative framework — including Logistic Regression and XGBoost baselines — designed to balance predictive performance, calibration quality, explainability, and subgroup fairness.

The model outputs calibrated probabilities suitable for downstream clinical risk stratification workflows. LightGBM was selected as the best-calibrated model among those evaluated in this framework (Brier Score: 0.146), making it well-suited for clinical risk stratification where probability estimates matter as much as ranking accuracy.

This model is released alongside a fully reproducible pipeline and open-source implementation to facilitate independent validation and reuse.

📊 Dataset

Property	Value
Source	MIMIC-IV (v2.2)
Total admissions	415,231
30-day readmission prevalence	~18%
Feature count	26 structured clinical features
Split	Train / Validation / Test (temporal split)

Features include demographics, admission type, primary diagnosis category, comorbidity burden (Elixhauser), length of stay, lab value summaries, procedure counts, and prior utilization history.

⚙️ Training

Setting	Value
Framework	LightGBM 4.x
Objective	Binary cross-entropy
Class imbalance	Scale-pos-weight tuned to prevalence
Hyperparameter tuning	Optuna (Bayesian search)
Calibration	Platt scaling (post-hoc)

📈 Performance

Metric	Value	Notes
AUC-ROC	~0.689	Discrimination performance
Brier Score	0.146	Best calibrated in the framework

✅ LightGBM achieves the best calibration among all models evaluated in this framework. Well-calibrated probabilities are critical in clinical settings where risk thresholds drive care decisions.

🔍 Explainability

Per-patient explanations are generated using SHAP TreeExplainer, which is exact and computationally efficient for tree-based models.

• Global feature importance via SHAP summary plots
• Local patient-level force plots for individual predictions
• Compatible with standard clinical decision support workflows

⚖️ Fairness Evaluation

The model was evaluated across 16 demographic and clinical subgroups, including stratifications by age group, sex, race/ethnicity, insurance type, and admission source.

All subgroups satisfy the following thresholds:

Metric	Threshold
ΔAUC (vs. overall)	≤ 0.05
ΔFNR (vs. overall)	≤ 0.10

No subgroup exhibited clinically meaningful performance degradation under these criteria.

🚀 Usage

import joblib
import numpy as np

# Load model
model = joblib.load("lightgbm.pkl")

# Replace with your 26 clinical features
X = np.array([[...]])

# Returns 30-day readmission probability
pred = model.predict_proba(X)[0][1]

print(f"Readmission risk: {pred:.3f}")

⚠️ Input features must match the 26 clinical variables used during training. See the repository for the full feature schema and preprocessing pipeline.

🎯 Intended Use

• Research and reproducibility
• Clinical ML benchmarking
• Demonstration of explainable and fair AI systems

🔁 Reproducibility

All results are fully reproducible using the open-source pipeline at github.com/Tomisin92/readmission-prediction, which includes data preprocessing, feature engineering, model training, SHAP explainability, and fairness auditing.

⚠️ Limitations

• Retrospective validation only — model was trained and evaluated on historical MIMIC-IV data; prospective validation has not been performed.
• Single institution — MIMIC-IV reflects one academic medical center (BIDMC); generalizability to other institutions requires local validation.
• No causal claims — feature associations do not imply clinical causation.
• Requires local validation before any deployment in a clinical decision support context.
• Credentialed dataset — MIMIC-IV requires PhysioNet credentialing; this model card does not distribute the underlying data.

🔗 Links

• 📄 Paper: arXiv:2604.22535
• 💻 Code: github.com/Tomisin92/readmission-prediction

📜 Citation

@misc{adisa2025readmission,
  title={Hospital Readmission Prediction with Explainability and Fairness},
  author={Adisa, Isaac Tosin},
  year={2026},
  eprint={2604.22535},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

License

This model is released under the MIT License. The underlying MIMIC-IV dataset is subject to its own PhysioNet credentialed access agreement.