Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure

Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure

Abstract Heart failure (HF) is a highly heterogeneous condition, and current methods struggle to synthesize extensive clinical data for personalized care. Using data from 343 HF patients, we developed mechanistic computational models of the cardiovascular system to create digital twins. These twins,...

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Main Authors: Feng Gu, Andrew J. Meyer, Filip Ježek, Shuangdi Zhang, Tonimarie Catalan, Alexandria Miller, Noah A. Schenk, Victoria E. Sturgess, Domingo Uceda, Rui Li, Emily Wittrup, Xinwei Hua, Brian E. Carlson, Yi-Da Tang, Farhan Raza, Kayvan Najarian, Scott L. Hummel, Daniel A. Beard
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01501-9
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https://doi.org/10.1038/s41746-025-01501-9

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