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Call for Papers

posted Sep 2, 2016, 2:19 PM by NIPS MLHC   [ updated Sep 12, 2016, 6:47 PM ]

NIPS 2016 Workshop on Machine Learning for Health (NIPS ML4HC)


http://www.nipsml4hc.ws/


A workshop at the Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2016) in Barcelona, Spain

Fri Dec 9th 08:00 AM -- 06:30 PM


DATES:

Sept 22, 2016: NIPS ML4HC Travel Award Submission Deadline

Oct 3, 2016: Round 1 Acceptance Notification (coinciding with NIPS Early Registration Deadline)

Oct 3, 2016: NIPS Early Registration Deadline

Oct 28, 2016: Round 2 Submission Deadline

Nov 11 2016: Round 2 Acceptance Notification

Dec 1, 2016: Final papers due

Dec 9, 2016: Workshop date


ABSTRACT:

The last decade has seen unprecedented growth in the availability and size of digital health data, including electronic health records, genetics, and wearable sensors. These rich data sources present opportunities to develop and apply machine learning methods to enable precision medicine. The aim of this workshop is to engender discussion between machine learning and clinical researchers about how statistical learning can enhance both the science and the practice of medicine.


Of particular interest to this year’s workshop is a phrase recently coined by the British Medical Journal, "Big Health Data", where the focus is on modeling and improving health outcomes across large numbers of patients with diverse genetic, phenotypic, and environmental characteristics. The majority of clinical informatics research has focused on narrow populations representing, for example, patients from a single institution or sharing a common disease, and on modeling clinical factors, such as lab test results and treatments. Big health considers large and diverse cohorts, often reaching over 100 million patients in size, as well as environmental factors that are known to impact health outcomes, including socioeconomic status, health care delivery and utilization, and pollution. Big Health Data problems pose a variety of challenges for standard statistical learning, many of them nontraditional. Including a patient’s race and income in statistical analysis, for example, evokes concerns about patient privacy. Novel approaches to differential privacy may help alleviate such concerns. Other examples include modeling biased measurements and non-random missingness and causal inference in the presence of latent confounders.


In this workshop we will bring together clinicians, health data experts, and machine learning researchers working on healthcare solutions. The goal is to have a discussion to understand clinical needs and the technical challenges resulting from those needs including the development of interpretable techniques which can adapt to noisy, dynamic environments and the handling of biases inherent in the data due to being generated during routine care.


SUBMISSION INSTRUCTIONS:


Researchers interested in contributing should upload an extended abstract of 4 pages in PDF format to the MLCB submission web site: https://easychair.org/conferences/?conf=nips16mlhc

by October 28, 2016, 11:59pm (time zone of your choice).


No special style is required. Authors should use the NIPS style file, and submissions should be suitably anonymized and meet the requirements for double-blind reviewing. Send any questions to nips16mlhc@gmail.com.


All submissions will be anonymously peer reviewed and will be evaluated on the basis of their technical content. The workshop allows submissions of papers that are under review or have been recently published in a conference or a journal. Authors should state any overlapping published work at time of submission.


Part of our workshop includes a clinician pitch, a five-minute presentation of open clinical problems that need data-driven solutions. These presentations will be followed by a discussion between invited clinicians and attending ML ­researchers to understand how machine learning can play a role in solving the problem presented. Finally, the pitch plays a secondary role of enabling new collaborations between machine learning researchers and clinicians: an important step for machine learning to have a meaningful role in healthcare. A general call for clinician pitches will be disseminated to clinical researchers and major physician organizations, including clinician social networks such as Doximity.


We will invite submission of two­ page abstracts (not including references) for poster contributions and short oral presentations describing innovative machine learning research on relevant clinical problems and data. 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, causality, model biases, transfer learning, and incorporation of non-clinical (e.g., socioeconomic) factors.



CONFIRMED SPEAKERS:

Leo Anthony Celi (MIMIC Project Lead)

Eric Xing (CMU)

Jenna Wiens (University of Michigan)

Sendhil Mullainathan (Harvard)

Julien Cornebise (DeepMind)

Neil Lawrence (University of Sheffield)

Joel Dudley (Mount Sinai)

Niels Peek (University of Manchester)


ORGANIZERS:

Jason Fries (Stanford)

Madalina Fiterau (Stanford)

Marzyeh Ghassemi (MIT)

Theofanis Karaletsos (Geometric Intelligence)

Rajesh Ranganath (Princeton)

Peter Schulam (Johns Hopkins)

Uri Shalit (NYU)

David Kale (University of Southern California)


SENIOR PC:

Artur Dubrawski (CMU)

Christopher Re (Stanford)

Cynthia Rudin (MIT)

David Sontag (NYU)

Deborah Estrin (Cornell-tech)

Fei Wang (Cornell)
George M Hripcsak (Columbia University)

Gunnar Rätsch (ETH)

Jimeng Sun (Georgia Institute of Technology)

Joyce Ho (Emory)

Neil Lawrence (U Sheffield)

Nigam Shah (Stanford)

Rosalind Picard (MIT)
Samantha Kleinberg (Stevens Institute)

Scott Delp (Stanford)

Suchi Saria (JHU)

Susan Murphy (U. Michigan)

Trevor Hastie (Stanford)

Zak Kohane (Harvard)


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