Probabilistic Models for Exploring, Predicting, and Influencing Health Trajectory Data

Peter Schulam, Johns Hopkins University
Host: Suchi Saria

We present novel probabilistic models for exploring, predicting, and controlling health trajectory data. The models address two important challenges that we must face when learning from health trajectory data. Solutions to these two challenges are the unifying arc of the thesis. First, we must account for unexplained heterogeneity. In many diseases, two individuals with the same diagnosis and otherwise similar characteristics (e.g. age, sex, lifestyle, and so on) can express the disease in very different ways. Well-known diseases that exhibit unexplained heterogeneity include asthma and autoimmune diseases such as multiple sclerosis and lupus. The implication for applied machine learning is that we may not always have sufficient observed information to make accurate predictions (i.e. we are missing some important features, or inputs, to the model). One key contribution in this thesis is a framework for building random-effects models of health trajectories that help us to solve the unexplained heterogeneity problem. Second, we must consider how treatment policies affect what we learn from health trajectory data and the questions that our models can answer. We argue that many prediction problems in healthcare cannot be addressed using supervised learning algorithms, but instead need to be tackled using techniques that can make “what if?” predictions. The issue stems from a mismatch between how patients were treated in the training data and how they are treated at test-time once the model is deployed. Another key contribution of this thesis is a strategy for addressing this sensitivity to treatment policies at train-time that connects ideas from causal inference and machine learning.

Speaker Biography

Peter Schulam is a PhD candidate in the Computer Science Department at Johns Hopkins University where he is working with Professor Suchi Saria. His research interests lie at the intersection of machine learning, statistical inference, and healthcare with an emphasis on developing methods to support the personalized medicine initiative. Before coming to JHU, he received his MS from Carnegie Mellon’s School of Computer Science and his BA from Princeton University. He was awarded a National Science Foundation Graduate Research Fellowship and the Dean’s Centennial Fellowship within Johns Hopkins’ Whiting School of Engineering.