The field of women's endocrinology has trailed behind data-driven medical solutions, largely due to concerns over the privacy of patient data. Valuable datapoints about hormone levels or menstrual cycling could expose patients who suffer from comorbidities or terminate a pregnancy, violating their privacy. We explore the application of Federated Learning (FL) to predict the optimal drug for patients with polycystic ovary syndrome (PCOS). PCOS is a serious hormonal disorder impacting millions of women worldwide, yet it's poorly understood and its research is stunted by a lack of patient data. We demonstrate that a variety of FL approaches succeed on a synthetic PCOS patient dataset. Our proposed FL models are a tool to access massive quantities of diverse data and identify the most effective treatment option while providing PCOS patients with privacy guarantees.
@article{arxiv.2308.11220,
title = {Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment},
author = {Lucia Morris and Tori Qiu and Nikhil Raghuraman},
journal= {arXiv preprint arXiv:2308.11220},
year = {2023}
}