Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment
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. Our code is open-sourced at https://github.com/toriqiu/fl-pcos.
Cite
Text
Morris et al. "Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment." NeurIPS 2022 Workshops: SyntheticData4ML, 2022.Markdown
[Morris et al. "Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment." NeurIPS 2022 Workshops: SyntheticData4ML, 2022.](https://mlanthology.org/neuripsw/2022/morris2022neuripsw-federated/)BibTeX
@inproceedings{morris2022neuripsw-federated,
title = {{Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment}},
author = {Morris, Lucia and Qiu, Tori and Raghuraman, Nikhil},
booktitle = {NeurIPS 2022 Workshops: SyntheticData4ML},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/morris2022neuripsw-federated/}
}