Resolving accuracy-fairness trade-offs in clinical decision support: A heterogeneous neural energy-based framework for diabetes management

Résumé fourni par la source

Background Clinical AI models frequently exhibit degraded performance and systemic bias when applied to highly imbalanced healthcare datasets. Navigating the inherent trade-off between predictive fidelity and demographic equity remains a critical challenge for translational medicine. To address this, we propose the Heterogeneous Neural Energy-Based Classifier ( ), a novel dual-paradigm framework. Methods The proposed architecture integrates a Dynamic Class Matrix ( ) for adaptive under-sampling with Simulated Annealing ( ) for robust hyperparameter optimization across non-convex loss landscapes. Additionally, an Adaptive Fairness Regulation ( ) module is incorporated to explicitly penalize demographic parity deviations during gradient-based optimization, ensuring equitable model convergence. Results Empirical evaluations on the Diabetes 130-US Hospitals dataset demonstrate that the deep gradient-based configuration ( ) achieves highly robust predictive performance in medication prescription, yielding an Area Under the Receiver Operating Characteristic ( ) of , an accuracy of , and a Matthews Correlation Coefficient ( ) of . Crucially, the AFR module curtails the Predicted Positive Rate ( ) disparity to , effectively mitigating algorithmic bias. Conversely, for 30-day readmission prediction, the gradient-free heuristic configuration ( ) prioritizes rapid optimization (completing evaluation in ) and strict demographic equity ( ), accepting an anticipated trade-off in global discriminative power ( , ). Conclusions The framework offers a mathematically rigorous and adaptable methodology for trustworthy clinical decision support. By balancing highly discriminative capabilities with demonstrable algorithmic fairness, it establishes a reliable paradigm for deploying equitable AI interventions in endocrinology and broader healthcare networks.

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