A framework towards digital twins for type 2 diabetes

作者：Yue Zhang, Guangrong Qin, Boris H. Aguilar, Noa Rappaport, James T. Yurkovich, Lance T. Pflieger, Sui Huang, Leroy Hood, Ilya Shmulevich · 发表于：Frontiers in Digital Health · 年份：2024 · DOI：10.3389/fdgth.2024.1336050 · 被引用次数：43 · 研究领域：Machine Learning in Healthcare、Digital Transformation in Industry、Artificial Intelligence in Healthcare and Education

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.