A framework towards digital twins for type 2 diabetes.

Document Type

Article

Publication Date

1-1-2024

Publication Title

Front Digit Health

Keywords

digital twin; knowledge graph; machine learning; precision medicine; type 2 diabetes.; washington; isb; machine learning

Abstract

INTRODUCTION: A digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.

METHODS: Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic-disease relationships.

RESULTS AND DISCUSSION: Our findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.

Clinical Institute

Kidney & Diabetes

Department

Nephrology

Department

Endocrinology

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