A novel machine learning based framework for developing composite digital biomarkers of disease progression
Measures from DHTs show promise in tracking neurodegenerative disease progression with increased sensitivity and reduced variability as compared to traditional clinical scores. Herein, we present a novel framework and methodology to construct composite digital measure of disease progression from high-dimensional DHT datasets, which may have utility in accelerating the development and application of composite digital biomarkers in drug development.
Authors
- James J. FitzGerald (ORCID: https://orcid.org/0000-0001-5980-9830)
- Andy Liaw
- Richard Baumgartner (ORCID: https://orcid.org/0000-0003-3330-8477)
- Marissa F. Dockendorf (ORCID: https://orcid.org/0000-0002-6341-0221)
- Jie Ren (ORCID: https://orcid.org/0000-0002-7061-1881)
- Chrystalina A. Antoniades (ORCID: https://orcid.org/0000-0002-1192-3834)
- Yuting Xu (ORCID: https://orcid.org/0000-0003-2091-3854)
- Vladimir Svetnik (ORCID: https://orcid.org/0000-0003-1561-2387)
- Judong Shen (ORCID: https://orcid.org/0000-0001-6150-1034)
- Song Zhai (ORCID: https://orcid.org/0000-0002-1900-7048)
- Dan Holder
Institutions
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- University of Oxford (GB)
- Decision Sciences (United States) (US)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-01-10
- DOI
- https://doi.org/10.3389/fdgth.2024.1500811
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00