Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

ABSTRACT:

Introduction

Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis.

Methods

This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal

SUBMITTER: Zad Z 

PROVIDER: S-EPMC10866556 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature