The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era

• Published in: NPJ digital medicine Vol. 6; no. 1; p. 132
• Main Authors: Wen, Andrew, He, Huan, Fu, Sunyang, Liu, Sijia, Miller, Kurt, Wang, Liwei, Roberts, Kirk E., Bedrick, Steven D., Hersh, William R., Liu, Hongfang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21-07-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2163; 631/114/2164; 692/308/575; Biomedicine; Biotechnology; Computer based modeling; Datasets; Digital technology; Genetic testing; Genotype & phenotype; Health informatics; Medicine; Medicine & Public Health; Perspective
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-023-00878-9

Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means. Nevertheless, current in-silico phenotyping development tends to be focused on a single phenotyping task resulting in a dearth of reusable tools supporting cross-task generalizable in-silico phenotyping. In addition, in-silico phenotyping remains largely inaccessible for a substantial portion of potentially interested users. Here, we highlight the barriers to the usage of in-silico phenotyping and potential solutions in the form of a framework of several desiderata as observed during our implementation of such tasks. In addition, we introduce an example implementation of said framework as a software application, with a focus on ease of adoption, cross-task reusability, and facilitating the clinical phenotyping algorithm development process.