IsaacT1992/hospital-readmission-logistic-regression

🏥 Hospital Readmission Prediction (Logistic Regression)

Author: Isaac Tosin Adisa

📌 Overview

This model predicts 30-day hospital readmission risk using structured clinical features derived from the MIMIC-IV dataset. It serves as the linear baseline in an integrated multi-model comparative framework alongside XGBoost and LightGBM, designed to evaluate the trade-offs between model complexity, predictive performance, calibration quality, explainability, and subgroup fairness.

The model outputs calibrated probabilities suitable for downstream clinical risk stratification workflows. As a logistic regression model, it offers strong interpretability by design — coefficients map directly to feature-level log-odds, making it transparent and auditable without requiring post-hoc explanation tools.

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	scikit-learn
Solver	lbfgs
Regularization	L2 (tuned via cross-validation)
Class imbalance	class_weight="balanced"
Feature scaling	StandardScaler (applied pre-fit)
Calibration	Platt scaling (post-hoc)

📈 Performance

Metric	Value	Notes
AUC-ROC	~0.67	Linear baseline discrimination

📊 Logistic Regression serves as the interpretable linear baseline in this framework. Its performance provides a lower-bound reference for evaluating the marginal gains of tree-based models (XGBoost, LightGBM) against the cost of reduced transparency.

🔍 Explainability

Logistic Regression is inherently interpretable — no post-hoc explanation method is required.

• Feature coefficients directly encode the direction and magnitude of each variable's contribution
• Odds ratios can be derived directly from model weights
• Compatible with standard clinical audit and regulatory review 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("logreg.pkl")

# Replace with your 26 clinical features (must be StandardScaler-transformed)
X = np.array([[...]])

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

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

⚠️ Input features must be scaled using the same StandardScaler fitted during training before inference. See the repository for the full feature schema and preprocessing pipeline.

🎯 Intended Use

• Linear baseline benchmarking against tree-based models
• Clinical ML interpretability research
• Demonstration of explainable and fair AI systems
• Reproducibility and model comparison

⚠️ Limitations

• Linear model — logistic regression cannot capture non-linear feature interactions present in complex clinical data; tree-based models may outperform it on discrimination metrics.
• 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.