Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment
This addresses privacy concerns in women's endocrinology research, enabling access to diverse data for PCOS treatment without exposing sensitive patient information.
The paper tackled the problem of predicting optimal drug treatments for polycystic ovary syndrome (PCOS) while protecting patient privacy, and demonstrated that federated learning approaches succeeded on a synthetic PCOS dataset.
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.