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NIPS ML4H 2017 : NIPS Workshop on Machine Learning for Health


When Dec 8, 2017 - Dec 8, 2017
Where Long Beach, CA
Submission Deadline Oct 30, 2017
Notification Due Nov 10, 2017
Final Version Due Dec 1, 2017
Categories    machine learning   bioinformatics

Call For Papers

NIPS Workshop on Machine Learning for Health (NIPS ML4H 2017)

A workshop at the Thirty-First Annual Conference on Neural Information
Processing Systems (NIPS 2017).

Friday, December 8, 2017
Long Beach Convention Center, Long Beach, CA, USA

Please direct questions to:

NOTE 2017/09/28: NIPS 2017 workshop registrations are now sold out. If you
have not registered you may still submit a paper. During submission, please
indicate an author that will attend or could attend in the unlikely event
that more registrations became available as a "corresponding author."


* Mon Oct 30, 2017: Submission deadline at 11:59PM
* Fri Nov 10, 2017: Acceptance notification (Poster or Spotlight+Poster)
* Thu Nov 16, 2017: NIPS deadline to cancel registration (with full refund)
* Fri Dec 01, 2017: Final papers posted online (with permission)
* Fri Dec 08, 2017: Workshop


What parts of Healthcare are Ripe for Disruption by Machine Learning Right

The goal of the Machine Learning for Health Workshop (NIPS ML4H 2017) is to
foster collaborations that meaningfully impact medicine by bringing
together clinicians, health data experts, and machine learning researchers.
We aim to build on the success of the last two NIPS ML4H workshops which
were widely attended and helped form the foundations of a new research

This year’s program emphasizes identifying previously unidentified problems
in healthcare that the machine learning community hasn't addressed, or
seeing old challenges through a new lens. While healthcare and medicine are
often touted as prime examples for disruption by AI and machine learning,
there has been vanishingly little evidence of this disruption to date. To
interested parties who are outside of the medical establishment (e.g.
machine learning researchers), the healthcare system can appear byzantine
and impenetrable, which results in a high barrier to entry. In this
workshop, we hope to reduce this activation energy by bringing together
leaders at the forefront of both machine learning and healthcare for a
dialog on areas of medicine that have immediate opportunities for machine
learning. Attendees at this workshop will quickly gain an understanding of
the key problems that are unique to healthcare and how machine learning can
be applied to addressed these challenges.

The workshop will feature invited talks from leading voices in both
medicine and machine learning. Invited clinicians will discuss open
clinical problems where data-driven solutions can make an immediate
difference. The workshop will conclude with an interactive panel discussion
where all speakers respond to questions provided by the audience.

From the research community, we welcome short paper submissions
highlighting novel research contributions at the intersection of machine
learning and healthcare. Accepted submissions will be featured as poster
presentations and (in select cases) as short oral spotlight presentations.


Researchers interested in contributing should upload short, anonymized
papers of up to 4 pages in PDF format by Monday, October 30, 2017, 11:59 PM
in the timezone of your choice.

Please submit via our ML4H EasyChair website:

Papers should adhere to the NIPS conference paper format, via the NIPS
LaTeX style file:

Workshop papers should be at most 4 pages of content, including text and
figures. Additional pages containing only bibliographic references can be
included without penalty.

#### Relevant Topics

Submitted papers should describe innovative machine learning research
focused on relevant problems in health and medicine. This can mean new
models, new datasets, new algorithms, or new applications. Topics of
interest include but are not limited to reinforcement learning, temporal
models, deep learning, semi-supervised learning, data integration, learning
from missing or biased data, learning from non-stationary data, model
criticism, model interpretability, causality, model biases, and transfer

#### Peer Review and Acceptance Criteria

All submissions will undergo double-blind peer review. It will be up to the
authors to ensure the proper anonymization of their paper. Do not include
any names or affiliations. Refer to your own past work in the third-person.

Accepted papers will be chosen based on technical merit and suitability to
the workshop's goals. All accepted papers will be included in one of two
poster presentation sessions on the day of the workshop. Some accepted
papers will be invited to give short oral spotlight presentations at the

#### Registration and Attendance

To promote community interaction, we hope at least one presenting author
has registered and can attend the workshop. However, because NIPS workshop
registration has sold out, we encourage all researchers to submit a paper
regardless of their registration status.

Accepted papers that cannot attend will at least be listed on our website.
It is unlikely that we will be able to create new registration spots for
accepted papers, but we are exploring possibilities. If your paper is
accepted and you cannot attend due to registration or other issues, please
contact us after you are accepted and we'll find solutions on a
case-by-case basis. Acceptance notifications will go out a few days before
the deadline for full refunds.

#### Copyright for Accepted Papers

This workshop will be informally published online but not officially
archived. This means:

* Authors will retain full copyright of their papers.

* Acceptance to NIPS ML4H 2017 does not preclude publication of the same
material in another journal or conference.

We encourage (but do not require) accepted papers to be posted on arXiv.
With author permission, we will post links to accepted short papers on our
workshop website.

Our workshop does allow submission of papers that are under review or have
been recently published in a conference or a journal. Authors should
clearly state any overlapping published work at time of submission.

Related Resources

NIPS 2017   The Thirty-first Annual Conference on Neural Information Processing Systems
ICPR 2018   24th International Conference on Pattern Recognition
ADAH 2017   Advanced Data Analytics in Health
ECCV 2018   European Conference on Computer Vision
PAKDD 2018   The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining
ETHE Blearning 2017   Blended learning in higher education: research findings
MLDM 2018   14th International Conference on Machine Learning and Data Mining MLDM 2018
NIPS 2018   The Thirty-second Annual Conference on Neural Information Processing Systems
CVPR 2018   Computer Vision and Pattern Recognition
COLT 2018   Computational Learning Theory