General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data
Abstract In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.
Authors
- Severine Soltani (ORCID: https://orcid.org/0000-0003-0894-9993)
- Benjamin L. Smarr (ORCID: https://orcid.org/0000-0003-4442-3956)
- İlkay Altıntaş (ORCID: https://orcid.org/0000-0002-2196-0305)
- Wendy Hartogensis (ORCID: https://orcid.org/0000-0002-4979-8661)
- Ashley E. Mason (ORCID: https://orcid.org/0000-0002-8744-0185)
- Frederick Hecht (ORCID: https://orcid.org/0000-0002-5782-1171)
- Subhasis Dasgupta (ORCID: https://orcid.org/0000-0002-0754-0515)
- Amarnath Gupta (ORCID: https://orcid.org/0000-0003-0897-120X)
- Varun Viswanath (ORCID: https://orcid.org/0000-0001-6299-5777)
- Stephan Dilchert (ORCID: https://orcid.org/0000-0001-6733-576X)
- Patrick Kasl (ORCID: https://orcid.org/0000-0002-7756-7326)
- Lauryn Keeler Bruce (ORCID: https://orcid.org/0000-0002-1262-9127)
- Jamison H. Burks (ORCID: https://orcid.org/0000-0003-4816-5704)
Institutions
- Baruch College (US)
- University of California, San Francisco (US)
- San Diego Supercomputer Center (US)
- UC San Diego Health System (US)
- University of California San Diego (US)
Publication Details
- Journal
- npj Women s Health
- Published
- 2024-11-06
- DOI
- https://doi.org/10.1038/s44294-024-00041-z
- Citations
- 2
- Primary Topic
- Mobile Health and mHealth Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.70
Funders
- U.S. Department of Defense
- National Metal and Materials Technology Center
- U.S. National Library of Medicine
- U.S. Army Medical Research and Development Command