[gov3009-l] Applied Stats Workshop TOMORROW (5/1) - Michael Hughes

Dear workshop community, We will convene for the Harvard University Applied Statistics Workshop (Gov 3009) TOMORROW on Wednesday (5/1). *Note: This will be the last workshop meeting of the 2018/2019 academic year. * The speaker is* Michael Hughes *(Tufts Engineering) who will be presenting his latest work, "Discovering Disease Subtypes that Improve Treatment Predictions: Prediction-Constrained Topic Models for Personalized Medicine". *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, May 1st at 12 noon - 1:30 pm. *Abstract: *For complex diseases like depression, choosing a successful treatment from several possible drugs remains a trial-and-error process in current clinical practice. By applying statistical machine learning to the electronic health records of thousands of patients, can we discover subtypes of disease which both improve population-wide understanding and improve patient-specific drug recommendations? One popular approach is to represent noisy, high-dimensional health records as mixtures of low-dimensional subtypes via a probabilistic topic model. I will introduce this common dimensionality reduction method and explain how off-the-shelf topic models are misspecified for downstream prediction tasks across many domains from text analysis to healthcare. To overcome these poor predictions, I will introduce a new framework -- prediction-constrained training -- which learns interpretable topic models that offer competitive drug recommendations. I will also discuss open challenges in using machine learning to improve clinical decision-making. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-CU8HWrQPpe9pjTw/viewform?usp=sf_link>. Workshop listserv sign-up at this link <https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.