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 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
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