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

08/22/2023

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

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Unfolding Values through Systematic Guidance: Conducting a Value-Centered Participatory Workshop for a Patient-Oriented Data Donation

In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristics

Federated Learning on Heterogenous Data using Chest CT

Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data

Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

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

Related Research

Towards the Practical Utility of Federated Learning in the Medical Domain

Privacy-preserving patient clustering for personalized federated learning

Unfolding Values through Systematic Guidance: Conducting a Value-Centered Participatory Workshop for a Patient-Oriented Data Donation

In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristics

Federated Learning on Heterogenous Data using Chest CT

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