Comparison of machine learning models for the prediction of hypertension in transgender patients undergoing gynecologic surgery
Machine learning models can be trained on smaller, selectively transgender populations and may perform similarly or better to predict cardiovascular outcomes in transgender patients, than models developed on predominantly cisgender patients; this can be useful in lower-resource settings with smaller-volume transgender patients.
Authors
- Alice Lin (ORCID: https://orcid.org/0000-0002-2526-477X)
- Jordan Franklin (ORCID: https://orcid.org/0000-0002-0992-6673)
- Aditi Vichare (ORCID: https://orcid.org/0000-0001-5070-8603)
- Xiaotian Yu (ORCID: https://orcid.org/0000-0002-7761-2377)
- Reetam Ganguli (ORCID: https://orcid.org/0000-0001-5327-7036)
- Stephen Wagner (ORCID: https://orcid.org/0000-0002-7677-7663)
Institutions
- Georgia Institute of Technology (US)
- Harvard University (US)
- University of California, Los Angeles (US)
- Providence College (US)
- Brown University (US)
- Warren Alpert Foundation (US)
- University of Virginia (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1038/s43856-024-00603-x
- Citations
- 3
- Primary Topic
- LGBTQ Health, Identity, and Policy
- Type
- article
- Field-Weighted Citation Impact
- 1.93