Connecting Human Models Through Human Function: Toward a Common Computational Protocol for Whole-Person Modeling

This preprint proposes human function as a common computational coordinate for connecting heterogeneous models of the same person without replacing their domain-specific meanings. Disease and medical prediction models, physiological and mechanistic models, digital twins, psychological and cognitive models, movement and biomechanical models, nutrition and behavioral models, environmental models, devices, and AI systems can retain their native outputs while contributing to a shared, longitudinal interpretation of the same whole person. The proposed architecture distinguishes task and demand, evidence-supported human functional capacity, actual functional engagement, performance, cost, boundaries, recovery, evidence, prediction, and temporal update. The Unified Ontology of Human Function (UOHF) provides semantic identities and governed relations, while the Human Function World Model (HFWM) provides a person-bound computational architecture for coordinating heterogeneous model contributions over time. The paper develops a 1 + N recursive model architecture, separates model composition, computational coupling, evidence derivation, and task–capacity interpretation, and presents a traceable hypothetical cross-model case. It also defines a research agenda for evaluating semantic interoperability, measurement validity, combined computation, prediction, and practical usefulness. This work presents a conceptual computational architecture and research agenda. It does not claim universal model compatibility, completed validation of all human functional capacities, autonomous diagnosis or treatment, or demonstrated clinical benefit.