Electronic health records and high throughput measurement technologies are changing the practice of healthcare to become more algorithmic and data-driven. This offers an exciting opportunity for machine learning to impact healthcare. A key challenge, however, is the heterogeneity of disease expression across people; a model that works well for one patient may perform very poorly for another. One solution is to build personalized models that blend information from a population and from the current individual to provide tailored inferences.
This tutorial will discuss ideas from machine learning that enable personalization (useful for applications in education, retail, medicine and recommender systems more broadly). The tutorial will focus on applications in healthcare and medicine. We will cover:
- Bayesian hierarchical models
- Transfer learning and multi-resolution sharing
- Functional data analysis
- Causal inference and individualized treatment effects
- Potential outcomes
- Strategies for adjusting for confounding
- Sequential and time-varying treatments
- Bayesian estimation of individualized treatment response
- "Causal Risk" and What-if Reasoning
- Dynamic treatment regimes
- Estimating optimal treatment rules
- Connections to reinforcement learning
Ultimately, the goal is to build individual-specific decision support tools that enable a data-driven understanding of alternative interventions by answering "what if?" questions: e.g. what would happen if I gave this patient drug A vs. drug B?
Target audience: The majority of this tutorial will be targeted at an audience with basic machine learning knowledge. No background in medicine or health care is needed.
Learning objectives: - Become familiar with important computational problems in precision medicine and individualized health care, understand key ideas behind personalized machine learning, and become familiar with state-of-the-art techniques used to build personalized decision-making tools.
Suchi Saria (Johns Hopkins University)
Suchi Saria is an assistant professor of computer science, health policy and statistics at Johns Hopkins University. Her research interests are in statistical machine learning and computational healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing decision-making. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and individualize disease management in chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller. Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the ``AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science's ``Brilliant 10’’.
Peter Schulam (Johns Hopkins University)
Peter Schulam is a PhD student in computer science at Johns Hopkins University. His research interests include machine learning and its applications to healthcare. Peter has made methodological contributions to advancing the use of electronic health data for individualizing care in chronic diseases. His current work explores applications in autoimmune diseases. He has won the National Science Foundation (NSF) Graduate Research Fellowship and the Whiting School of Engineering Centennial Fellowship. He is working with Prof. Suchi Saria for his PhD. Prior to that, he received his master’s from Carnegie Mellon University and his bachelor’s from Princeton University.
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