The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data. Issue 1 (14th July 2020)

• Record Type: Journal Article
• Authors: Bent, Brinnae; Wang, Ke; Grzesiak, Emilia; Jiang, Chentian; Qi, Yuankai; Jiang, Yihang; Cho, Peter; Zingler, Kyle; Ogbeide, Felix Ikponmwosa; Zhao, Arthur; Runge, Ryan; Sim, Ida; Dunn, Jessilyn
• Is Part Of: Journal of clinical and translational science. Volume 5:Issue 1(2021)
• Publication Date: 2020-07-14
• Subjects: Biomedical informatics, -- wearable sensors, -- health information management, -- open source, -- digital medicine, -- digital health, -- mHealth; Clinical medicine -- Research -- Periodicals; Medicine, Experimental -- Periodicals; Human experimentation in medicine -- Periodicals; 616.027
• DOI: 10.1017/cts.2020.511
• Languages: English
• ISSNs: 2059-8661

Abstract

Abstract: Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. Methods: In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP) . Results: Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. Conclusions: The clear need for such a platform will accelerate the DBDP's adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.