Addressing Algorithmic Bias in Diabetes Care: A Multi-Modal Machine Learning Framework for Equitable Readmission Prediction

Diabetes readmissions affect 14.4-22.7% of patients, disproportionately impacting minority populations and costing $3.8 billion annually. Existing prediction models suffer from algorithmic bias and limited clinical interpretability. Develop a fairness-optimized multi-modal machine learning framework for equitable diabetes readmission prediction with actionable clinical insights. We designed a novel architecture integrating four specialized neural networks: clinical data processing through deep neural networks, medication interactions via graph neural networks, demographic factors through fairness-aware models, and temporal patterns using LSTM networks. Adversarial debiasing and multi-objective optimization ensured equitable performance across demographic groups. The framework was validated using 101,766 diabetes encounters from 130 US hospitals and pilottested in three healthcare systems. Results: The multi-modal framework achieved AUC-ROC of 0.923, representing 18.5% improvement over Random Forest baselines. Bias mitigation reduced demographic disparities by 85%, with all fairness metrics below 5% thresholds. SHAP analysis identified 15 key risk factors, 60% being clinically modifiable. Six-month pilot implementation demonstrated 23.7% readmission reduction and $2.3 million healthcare savings. This framework successfully integrates predictive accuracy, algorithmic fairness, and clinical interpretability, establishing a new paradigm for equitable healthcare AI that transforms diabetes care from reactive treatment to proactive prevention.