Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment

TL;DR

This paper demonstrates the successful application of federated learning to predict treatments for PCOS using synthetic patient data, addressing privacy concerns and enabling access to diverse datasets.

Contribution

It introduces federated learning models tailored for PCOS treatment prediction, providing a privacy-preserving approach to leverage multiple data sources.

Findings

FL models succeed on synthetic PCOS data

Approach preserves patient privacy

Enables access to diverse datasets for treatment prediction

Abstract

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.