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.

Keywords

Translational research, Data mining, High-throughput screening

Comments

Supplementary Materials

DOI

10.1038/s41746-023-00878-9

PMID

37479735

PMCID

PMC10362064

PubMedCentral® Posted Date

July 2023

PubMedCentral® Full Text Version

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