TY - JOUR
T1 - The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era
AU - Wen, Andrew
AU - He, Huan
AU - Fu, Sunyang
AU - Liu, Sijia
AU - Miller, Kurt
AU - Wang, Liwei
AU - Roberts, Kirk E.
AU - Bedrick, Steven D.
AU - Hersh, William R.
AU - Liu, Hongfang
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85165452198&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165452198&partnerID=8YFLogxK
U2 - 10.1038/s41746-023-00878-9
DO - 10.1038/s41746-023-00878-9
M3 - Article
AN - SCOPUS:85165452198
SN - 2398-6352
VL - 6
JO - npj Digital Medicine
JF - npj Digital Medicine
IS - 1
M1 - 132
ER -