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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

Sat Dec 08 05:00 AM -- 03:30 PM (PST) @ Room 517 D
Event URL: https://ml4health.github.io/2018/ »

Machine learning has had many notable successes within healthcare and medicine. However, nearly all such successes to date have been driven by supervised learning techniques. As a result, many other important areas of machine learning have been neglected and under appreciated in healthcare applications. In this workshop, we will convene a diverse set of leading researchers who are pushing beyond the boundaries of traditional supervised approaches. Attendees at the workshop will gain an appreciation for problems that are unique to healthcare and a better understanding of how machine learning techniques, including clustering, active learning, dimensionality reduction, reinforcement learning, causal inference, and others, may be leveraged to solve important clinical problems.

This year’s program will also include spotlight presentations and two poster sessions highlighting novel research contributions at the intersection of machine learning and healthcare. We will invite submission of two­ page abstracts (not including references) for poster contributions. Topics of interest include but are not limited to models for diseases and clinical data, temporal models, Markov decision processes for clinical decision support, multi­scale data-­integration, modeling with missing or biased data, learning with non-stationary data, uncertainty and uncertainty propagation, non ­i.i.d. structure in the data, critique of models, interpretable models, causality, model biases, transfer learning, and incorporation of non-clinical (e.g., socioeconomic) factors.

The broader goal of the NIPS 2018 Machine Learning for Health Workshop (ML4H) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. Attendees at this workshop can also expect to broaden their network of collaborators to include clinicians and machine learning researchers who are focused on solving some of the most import problems in medicine and healthcare.

Author Information

Andrew Beam (Harvard Medical School)
Tristan Naumann (Microsoft Research)
Marzyeh Ghassemi (University of Toronto)
Matthew McDermott (MIT)
Madalina Fiterau (UMass Amherst)

Madalina Fiterau is an Assistant Professor at the College of College of Information and Computer Sciences at UMass Amherst, with a focus on AI/ML. Previously, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University, working with Professors Chris Ré and Scott Delp in the Mobilize Center. Madalina has obtained a PhD in Machine Learning from Carnegie Mellon University in September 2015, advised by Professor Artur Dubrawski. The focus of her PhD thesis, entitled “Discovering Compact and Informative Structures through Data Partitioning”, is learning interpretable ensembles, with applicability ranging from image classification to a clinical alert prediction system. Madalina is currently expanding her research on interpretable models, in part by applying deep learning to obtain salient representations from biomedical “deep” data, including time series, text and images. Madalina is the recipient of the GE Foundation Scholar Leader Award for Central and Eastern Europe. She is the recipient of the Marr Prize for Best Paper at ICCV 2015 and of Star Research Award at the Annual Congress of the Society of Critical Care Medicine 2016. She has organized two editions of the Machine Learning for Clinical Data Analysis Workshop at NIPS, in 2013 and 2014.

Irene Y Chen (MIT)

Irene is a PhD student at MIT focusing on applications on health care and fairness. She did her undergrad at Harvard where I studied applied math and computational engineering. Before starting at MIT, she worked for two years at Dropbox as a data scientist and machine learning engineer.

Brett Beaulieu-Jones (Harvard Medical School)
Michael Hughes (Tufts University)
Farah Shamout (University of Oxford)
Corey Chivers (University of Pennsylvania)
Jaz Kandola (Imperial College London)
Alexandre Yahi (Columbia University)
Samuel Finlayson (Harvard Medical School)

Samuel Finlayson is a MD-PhD Candidate studying jointly at Harvard Medical School and Massachusetts Institute of Technology. His research focuses on developing machine learning methods for precision medicine. Current applications focus on neurological diseases and extend techniques from computer vision, natural language processing, and single-cell genomics. Previously, he studied Biomedical Informatics at Stanford University.

Bruno Jedynak (Portland state university)
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.

Natalia Antropova (The University of Chicago)
Jason Fries (Stanford University)
Adrian Dalca (MIT)
Irene Chen (MIT)

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