Rheumatic Digital Twin: Proposed Machine Learning-Based Multimodal Framework to Inform Clinical Decision-Making

Clinical Snapshot

Key Findings

• P Value: Not applicable - no hypothesis testing

• Effect Size: Not applicable - conceptual paper

• Primary Outcome: Theoretical framework description only - no empirical outcomes

• Nnt Or Sensitivity: Not applicable - no performance metrics provided

• Confidence Interval: Not applicable - no statistical analysis

Clinical Application

Unknown - significant computational infrastructure and data integration challenges likely Would require integration with Australian electronic health systems and compliance with privacy regulations; potential relevance to RACGP chronic disease management guidelines Potentially applicable to rheumatic disease patients but requires validation

Abstract

Rheumatic diseases are chronic, immune-mediated conditions characterized by significant heterogeneity in presentation and disease course. However, current clinical approaches often rely on snapshot-based assessments that fail to capture the complex longitudinal evolution of these conditions. To address these limitations and support the implementation of precision medicine, we present the design for the Rheumatic Digital Twin, a novel, modular conceptual framework intended to integrate heterogeneous multimodal data, ranging from electronic health records and clinical notes to imaging and omics, into a dynamic, computational representation of the patient journey. Our theoretical architecture addresses challenges related to data silos and variable availability of data modalities through a multistage approach that envisions the use of domain-specific foundation models to independently process distinct data modalities. To effectively model the temporal progression inherent in chronic diseases, the proposed design utilizes Transformer architectures, leveraging self-attention mechanisms to treat patient events, such as lab results or medication changes, as sequential data tokens. We describe how these unimodal representations would subsequently be fused via joint embedding techniques to construct a shared, multimodal representational space. Envisioned to function analogously to a recommender system, the Rheumatic Digital Twin framework is modeled to map patients into a latent space where proximity reflects clinical and biological similarity. By identifying "nearest neighbors," historical patients with comparable trajectories, the system aims to enable in silico cohorting, theoretically allowing clinicians to forecast key clinical events, predict treatment responses, and identify likely disease courses based on the outcomes of similar peers.

References

1. 1.Selani, D., Knevel, R., Reinders, M., & van den Akker, E. B. (2026). Rheumatic Digital Twin: Proposed Machine Learning-Based Multimodal Framework to Inform Clinical Decision-Making. Journal of Medical Internet Research. https://doi.org/10.2196/86763