Provenance-Aware Explainable Digital Twin for Personalized Health Management

Abstract

Abstract AI can now help with personalized prediction, tracking, and decision-making thanks to progress in data-driven health analytics. However, many models are still hard to understand in clinical settings. To address these limitations, our work presents the Provenance-Aware and Explainable Digital Twin (PA-XDT) framework, integrating a digital twin, explainable AI techniques, and transparent provenance tracking for patient-centered health management.PA-XDT uses a compact LSTM-based twin trained on short temporal sequences to model near-term physiological dynamics and quantify uncertainty. In the implemented system, this twin works alongside a Gradient Boosting classifier that provides stable risk predictions, supported by global and local SHAP analyses and twin-validated counterfactual checks. A lightweight provenance layer records hashed inputs, outputs, and explanation metadata, enabling verifiable audit trails. Experiments on a gallstone risk dataset show that this combined pipeline improves physiological coherence and maintains strong predictive performance.

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