Timezone: »

Machine Learning For Healthcare (MLHC)
Theofanis Karaletsos · Rajesh Ranganath · Suchi Saria · David Sontag

Fri Dec 11 05:30 AM -- 03:30 PM (PST) @ 510 bd
Event URL: https://sites.google.com/site/nipsmlhc15/ »

Recent years have seen an unprecedented rise in the availability and size of collections of clinical data such as electronic health records. These rich data sources present opportunities to apply and develop machine learning methods to solve problems faced by clinicians and to usher in new forms of medical practice that would otherwise be infeasible. The aim of this workshop is to foster discussions between machine learning researchers and clinicians of how machine learning can be used to address fundamental problems in health care.

Of particular interest to this year’s workshop is statistical modeling. The role of modeling in healthcare is two-fold. First, it provides clinicians with a tool to aid exploration of hypotheses in a data-driven way. Second, it furnishes evidence-based clinically actionable predictions. Examples include machine learning of disease progression models, where patients and diseases are characterized by states that evolve over time, or dose-response models, where the treatment details involving complex and often combinatorial therapies can be inferred in a data driven way to optimally treat individual patients. Such methods face many statistical challenges such as accounting for confounding effects like socioeconomic backgrounds or genetic alterations in subpopulations. Causal models learned from large collections of patient records, coupled with detailed patient specific data, enable precision medicine, wherein the models become in-silico testbeds for testing hypotheses for a single patient.

In this workshop we bring together clinicians and machine learning researchers working on healthcare solutions. The goal is to have a discussion to understand clinical needs and the technical challenges presented by the needs including interpretable techniques which can adapt to noisy, dynamic environments and the biases inherent in the data due to being generated by the current standard of care.

Part of our workshop includes a clinician pitch. This clinician pitch will be a general call disseminated to clinicians asking for a five minute presentation of problems they are trying to solve based on empirical data. The problem presentations will followed by a discussion between invited clinicians and attending ML-researchers to help refine them and understand how machine learning will play a role in solving them. Finally, the pitch plays a secondary role of fostering new collaborations between machine learning researchers and clinicians: an important step for machine learning’s role in healthcare.

We invite submissions of 2-page abstracts for poster contributions to the workshops and for short contributed talks. Topics of interest are: models for diseases and clinical data, temporal models, Markov decision processes for clinical decision support, multi-scale data-integration, issues with missing data, uncertainty and uncertainty propagation, non-i.i.d. structure in the data, critique of models, causality, model biases and transfer learning for healthcare.

Author Information

Theofanis Karaletsos (Memorial Sloan Kettering Cancer Center)
Rajesh Ranganath (Princeton University)

Rajesh Ranganath is a PhD candidate in computer science at Princeton University. His research interests include approximate inference, model checking, Bayesian nonparametrics, and machine learning for healthcare. Rajesh has made several advances in variational methods, especially in popularising black-box variational inference methods that automate the process of inference by making variational inference easier to use while providing more scalable, and accurate posterior approximations. Rajesh works in SLAP group with David Blei. Before starting his PhD, Rajesh worked as a software engineer for AMA Capital Management. He obtained his BS and MS from Stanford University with Andrew Ng and Dan Jurafsky. Rajesh has won several awards and fellowships including the NDSEG graduate fellowship and the Porter Ogden Jacobus Fellowship, given to the top four doctoral students at Princeton University.

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’’.

David Sontag (NYU)

More from the Same Authors