Design for a Digital Twin in Clinical Patient Care

TL;DR

This paper proposes a general Digital Twin design for clinical patient care that integrates knowledge graphs and ensemble learning to support decision-making across the patient's clinical journey.

Contribution

It introduces a novel, unspecialized Digital Twin framework combining knowledge graphs and ensemble learning for personalized clinical decision support.

Findings

The design is predictive, modular, and evolving.

It is informed, interpretable, and explainable.

Broad clinical applications are enabled.

Abstract

Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements emerging from established clinical workflows. We present a general and unspecialized Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient's clinical journey and assist clinicians in their decision-making. Such a design is predictive, modular, evolving, informed, interpretable and explainable, thus opening broad clinical applications.