Comparison of machine learning models for the prediction of hypertension in transgender patients undergoing gynecologic surgery.

ABSTRACT:

Background

Transgender patients face a higher burden of cardiovascular morbidity due to structural and biological stressors, particularly in low-resource settings. No studies exist comparing machine learning model development strategies for this unique patient cohort and limited literature exists comparing data/outcomes between transgender and cisgender populations.

Methods

We compare machine learning models trained solely on transgender patients against models developed on a size-matched and ratio-matched cohort of cisgender patients and a 300-fold larger, ratio-matched cohort of cisgender patients undergoing obstetric/gynecologic procedures in the National Surgical Quality Improvement Program from January 1, 2005 through December 31, 2019. All models were developed to pred

SUBMITTER: Ganguli R 

PROVIDER: S-EPMC11442826 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature