Identifying individuals at risk for weight gain using machine learning in electronic medical records from the <scp>United States</scp>
The current study demonstrates an ML approach applied to EMR to identify those at risk for weight gain over 12 months. Providers may use this risk stratification to prioritise prevention strategies or earlier obesity intervention.
Authors
- Scott Kahan (ORCID: https://orcid.org/0000-0002-3080-0661)
- Beverly L. Falcón
- Margaret Hoyt (ORCID: https://orcid.org/0000-0002-9486-2095)
- Ilya Lipkovich (ORCID: https://orcid.org/0000-0002-1936-2197)
- Christy R. Houle
- Casey Choong (ORCID: https://orcid.org/0000-0001-6476-784X)
- Neena Xavier (ORCID: https://orcid.org/0000-0002-6193-3950)
- C. Nowak
- Hong Kan
Institutions
- Eli Lilly (United States) (US)
- George Washington University (US)
- Washington Center for Weight Management and Research (US)
Publication Details
- Journal
- Diabetes Obesity and Metabolism
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1111/dom.16311
- Citations
- 1
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Eli Lilly and Company