Toward mechanistic medical digital twins: some use cases in immunology.
Document Type
Article
Publication Date
1-1-2024
Publication Title
Front Digit Health
Keywords
washington; isb; machine learning
Abstract
A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.
Department
Institute for Systems Biology
Recommended Citation
Laubenbacher, Reinhard; Adler, Fred; An, Gary; Castiglione, Filippo; Eubank, Stephen; Fonseca, Luis L; Glazier, James; Helikar, Tomas; Jett-Tilton, Marti; Kirschner, Denise; Macklin, Paul; Mehrad, Borna; Moore, Beth; Pasour, Virginia; Shmulevich, Ilya; Smith, Amber; Voigt, Isabel; Yankeelov, Thomas E; and Ziemssen, Tjalf, "Toward mechanistic medical digital twins: some use cases in immunology." (2024). Articles, Abstracts, and Reports. 8627.
https://digitalcommons.providence.org/publications/8627
