Fwd: CFP: NIPS 2016 Workshop on Machine Learning for Health

[Artur Dubrawski]

Team,

This is a good venue to consider submitting your semi-mature work into 
ML for healthcare.

Cheers
Artur


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CALL FOR PAPERS

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)
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

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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 at gmail.com 
<mailto:nips16mlhc at 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:

Madalina Fiterau (Stanford)
Jason Fries (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)
Gunnar Rätsch (ETH)
Jimeng Sun (Georgia Institute of Technology)
Joyce Ho (Emory)
Neil Lawrence (U Sheffield)
Nigam Shah (Stanford)
Rosalind Picard (MIT)
Scott Delp (Stanford)
Suchi Saria (JHU)
Susan Murphy (U. Michigan)
Trevor Hastie (Stanford)
Zak Kohane (Harvard)
Samantha Kleinberg (Stevens Institute)
George M Hripcsak (Columbia University)


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