Human Digital Twin for Health (HDT-H): An Applied Multiscale Framework for Early Diagnosis, In-Silico Clinical Simulation, and Prospective Validation Protocol

Human Digital Twin for Health (HDT-H) is an applied multiscale framework designed to support early diagnosis, personalized clinical simulation, and prospective validation in precision medicine. The framework integrates multimodal physiological data from wearable devices and electronic health records through cross-attention neural networks, Neural Ordinary Differential Equations (Neural ODEs), HL7-FHIR v5.0.0 interoperability, and executable PK/PD cardiovascular simulations. This work presents a mathematically formalized architecture together with a SPIRIT- and TRIPOD+AI-aligned prospective clinical validation protocol. To ensure scientific transparency, all projected performance metrics are explicitly identified as literature-based projections pending validation through prospective clinical studies. The proposed framework is intended as a research platform for Human Digital Twins and precision digital health rather than as a validated clinical decision-support system.