The Impact Framework and Implementation for Accessible in Silico Clinical Phenotyping in the Digital Era

Authors

Andrew Wen
Huan He
Sunyang Fu
Sijia Liu
Kurt Miller
Liwei Wang
Kirk E Roberts
Steven D Bedrick
William R Hersh
Hongfang Liu

Publication Date

7-21-2023

Journal

NPJ Digital Medicine

Abstract

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.

Comments

Supplementary Materials

PMID: 37479735