HA-PI-MADT: A Hybrid Adaptive Multimodal Digital Twin-Inspired Framework for Reliable Healthcare Prediction with Improved Ranking and Calibration Performance

Abstract

The integration of heterogeneous healthcare data sources remains a major challenge in developing reliable and personalized predictive systems for digital healthcare applications. Traditional machine learning methods perform well on structured clinical data but often fail to effectively exploit multimodal information, while deep learning approaches may suffer from instability, weak generalization, and poor calibration when dealing with limited modalities. To address these limitations, this study proposes HA-PI-MADT, a hybrid adaptive healthcare-informed multimodal digital twin-inspired framework that combines deep multimodal representation learning with ensemble-based predictive modeling for robust and trustworthy healthcare prediction. The proposed framework integrates wearable sensor signals, electronic health records (EHRs), CT/MRI imaging representations, and population-level risk prototypes derived from the UCI diabetes dataset within a unified multimodal healthcare representation architecture. In addition, a modality-aware adaptive fusion mechanism dynamically adjusts the contribution of each modality according to its relevance and data quality, while a hybrid stacking strategy combines deep multimodal embeddings with classical ensemble learners to improve predictive robustness and ranking performance. To enhance clinical trustworthiness, calibration-aware optimization is incorporated to improve probabilistic reliability and uncertainty estimation. Extensive experiments conducted on a multimodal healthcare dataset demonstrate that HA-PI-MADT achieves a balanced performance profile across discrimination, ranking, and calibration-oriented evaluation metrics compared with several unimodal, multimodal, and ensemble baselines. The proposed framework achieves strong ranking-oriented and classification performance, including the highest AUPRC (0.6388) and F1-score (0.6327), while also demonstrating competitive calibration-oriented reliability through lower Brier score and negative log-likelihood values. The results demonstrate the effectiveness of the proposed hybrid adaptive multimodal digital twin-inspired framework for reliable, robust, and clinically trustworthy healthcare prediction.