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

• URL: http://arxiv.org/abs/2308.11220v1
• Date: Tue, 22 Aug 2023 06:21:39 GMT
• Title: Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment
• Authors: Lucia Morris, Tori Qiu, Nikhil Raghuraman
• Abstract summary: We explore the application of Federated Learning 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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