Equitable machine learning to advance healthcare

October 15, 2020
3:00-4:00 pm ET
Sococo Halligan 102, Zoom
Speaker: Irene Chen, Ph.D. candidate, Massachusetts Institute of Technology
Host: Mike Hughes

Abstract

Machine learning (ML) has demonstrated the potential to fundamentally improve healthcare because of its ability to find latent patterns in large observational datasets and scale insights rapidly. However, the use of ML in healthcare also raises numerous ethical concerns, especially as models can amplify existing health inequities. In this talk, I outline two approaches to characterize inequality in ML and adapt models for patients without reliable access to healthcare. First, I decompose cost-based metrics of discrimination in supervised learning into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Second, I describe a deep generative model for disease subtyping while correcting for patient misalignment in disease onset time. I conclude with a pipeline for ethical machine learning in healthcare, ranging from problem selection to post- deployment considerations, and recommendations for future research.

Bio:

Irene Chen is a computer science PhD student at MIT, advised by David Sontag. Her research focuses on machine learning methods to improve clinical care and deepen our understanding of human health, with applications in areas such as heart failure and intimate partner violence. Her work has been published in both machine learning conferences (NeurIPS) and medical journals (Nature Medicine, AMA Journal of Ethics), and covered by media outlets including MIT Tech Review, NPR/WGBH, and Stat News. Prior to her PhD, Irene received her AB in applied math and SM in computation engineering from Harvard University.

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