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.
More from the Same Authors
2022 Poster: JAWS: Auditing Predictive Uncertainty Under Covariate Shift »
Drew Prinster · Anqi Liu · Suchi Saria
2021 : Discussion: Aleksander Mądry, Ernest Mwebaze, Suchi Saria »
Aleksander Madry · Ernest Mwebaze · Suchi Saria
2021 : Dataset Shifts: 8 Years of Going from Practice to Theory to Policy and Future Directions »
2019 Workshop: Learning Meaningful Representations of Life »
Elizabeth Wood · Yakir Reshef · Jonathan Bloom · Jasper Snoek · Barbara Engelhardt · Scott Linderman · Suchi Saria · Alexander Wiltschko · Casey Greene · Chang Liu · Kresten Lindorff-Larsen · Debora Marks
2018 Workshop: Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare »
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca · Irene Chen
2018 : Panel on research process »
Zachary Lipton · Charles Sutton · Finale Doshi-Velez · Hanna Wallach · Suchi Saria · Rich Caruana · Thomas Rainforth
2017 : Invited talk: Is interpretability and explainability enough for safe and reliable decision making? »
2017 Workshop: Machine Learning for Health (ML4H) - What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now? »
Jason Fries · Alex Wiltschko · Andrew Beam · Isaac S Kohane · Jasper Snoek · Peter Schulam · Madalina Fiterau · David Kale · Rajesh Ranganath · Bruno Jedynak · Michael Hughes · Tristan Naumann · Natalia Antropova · Adrian Dalca · SHUBHI ASTHANA · Prateek Tandon · Jaz Kandola · Uri Shalit · Marzyeh Ghassemi · Tim Althoff · Alexander Ratner · Jumana Dakka
2017 Poster: Reliable Decision Support using Counterfactual Models »
Peter Schulam · Suchi Saria
2017 Oral: Reliable Decision Support using Counterfactual Models »
Peter Schulam · Suchi Saria
2016 : Estimating What-if Outcomes for Targeting Interventions in a Clinical Setting »
2016 Workshop: Machine Learning for Health »
Uri Shalit · Marzyeh Ghassemi · Jason Fries · Rajesh Ranganath · Theofanis Karaletsos · David Kale · Peter Schulam · Madalina Fiterau
2016 Poster: Disease Trajectory Maps »
Peter Schulam · Raman Arora
2015 Workshop: Machine Learning For Healthcare (MLHC) »
Theofanis Karaletsos · Rajesh Ranganath · Suchi Saria · David Sontag
2015 Poster: A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure »
Peter Schulam · Suchi Saria