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Team,<br>
<br>
This is a good venue to consider submitting your semi-mature work
into ML for healthcare.<br>
<br>
Cheers<br>
Artur<br>
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<div class="">=============================================================================== </div>
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<div class="">CALL FOR PAPERS </div>
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<div class="">NIPS 2016 Workshop on Machine Learning for
Health (NIPS ML4HC)</div>
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href="http://www.nipsml4hc.ws/" class="">http://www.nipsml4hc.ws/</a></div>
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<div class="">A workshop at the Twenty-Ninth Annual Conference
on Neural Information Processing Systems (NIPS 2016)</div>
<div class="">Barcelona, Spain</div>
<div class="">Fri Dec 9th 08:00 AM -- 06:30 PM </div>
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<div class="">DATES:</div>
<div class="">Sept 22, 2016: NIPS ML4HC Travel Award
Submission Deadline</div>
<div class="">Oct 3, 2016: Round 1 Acceptance Notification
(coinciding with NIPS Early Registration Deadline)</div>
<div class="">Oct 3, 2016: NIPS Early Registration Deadline</div>
<div class="">Oct 28, 2016: Round 2 Submission Deadline</div>
<div class="">Nov 11 2016: Round 2 Acceptance Notification</div>
<div class="">Dec 1, 2016: Final papers due</div>
<div class="">Dec 9, 2016: Workshop</div>
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<div class="">ABSTRACT:</div>
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<div class="">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.</div>
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<div class="">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.</div>
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<div class="">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.</div>
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<div class="">SUBMISSION INSTRUCTIONS:</div>
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<div class="">Researchers interested in contributing should
upload an extended abstract of 4 pages in PDF format to the
MLCB submission web site: </div>
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href="https://easychair.org/conferences/?conf=nips16mlhc"
class="">https://easychair.org/conferences/?conf=nips16mlhc</a></div>
<div class="">by October 28, 2016, 11:59pm (time zone of your
choice).</div>
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<div class="">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 <a moz-do-not-send="true"
href="mailto:nips16mlhc@gmail.com" class="">nips16mlhc@gmail.com</a>.</div>
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<div class="">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.</div>
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<div class="">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.</div>
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<div class="">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.</div>
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<div class="">CONFIRMED SPEAKERS:</div>
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<div class="">Leo Anthony Celi (MIMIC Project Lead)</div>
<div class="">Eric Xing (CMU)</div>
<div class="">Jenna Wiens (University of Michigan)</div>
<div class="">Sendhil Mullainathan (Harvard)</div>
<div class="">Julien Cornebise (DeepMind)</div>
<div class="">Neil Lawrence (University of Sheffield)</div>
<div class="">Joel Dudley (Mount Sinai)</div>
<div class="">Niels Peek (University of Manchester)</div>
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<div class="">ORGANIZERS:</div>
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<div class="">Madalina Fiterau (Stanford)</div>
<div class="">Jason Fries (Stanford)</div>
<div class="">Marzyeh Ghassemi (MIT)</div>
<div class="">Theofanis Karaletsos (Geometric Intelligence)</div>
<div class="">Rajesh Ranganath (Princeton)</div>
<div class="">Peter Schulam (Johns Hopkins)</div>
<div class="">Uri Shalit (NYU)</div>
<div class="">David Kale (University of Southern California)</div>
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<div class="">SENIOR PC:</div>
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<div class="">Artur Dubrawski (CMU)</div>
<div class="">Christopher Re (Stanford)</div>
<div class="">Cynthia Rudin (MIT)</div>
<div class="">David Sontag (NYU)</div>
<div class="">Deborah Estrin (Cornell-tech)</div>
<div class="">Fei Wang (Cornell)</div>
<div class="">Gunnar Rätsch (ETH)</div>
<div class="">Jimeng Sun (Georgia Institute of Technology)</div>
<div class="">Joyce Ho (Emory)</div>
<div class="">Neil Lawrence (U Sheffield)</div>
<div class="">Nigam Shah (Stanford)</div>
<div class="">Rosalind Picard (MIT)</div>
<div class="">Scott Delp (Stanford)</div>
<div class="">Suchi Saria (JHU)</div>
<div class="">Susan Murphy (U. Michigan)</div>
<div class="">Trevor Hastie (Stanford)</div>
<div class="">Zak Kohane (Harvard)</div>
<div class="">Samantha Kleinberg (Stevens Institute)</div>
<div class="">George M Hripcsak (Columbia University)</div>
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