Fwd: [ML-news] Call for Papers - Machine Learning for Healthcare - Submission deadline: April 12, 2023

Tue Mar 21 21:04:29 EDT 2023

Good venue, this year's highlight is our own Ina Fiterau on the Program
Committee :)

Artur

---------- Forwarded message ---------
From: shalma... at gmail.com <shalmali.anu at gmail.com>
Date: Tue, Mar 21, 2023 at 8:54 PM
Subject: [ML-news] Call for Papers - Machine Learning for Healthcare -
Submission deadline: April 12, 2023
To: Machine Learning News <ml-news at googlegroups.com>

--------------------------------------------------------------------------------

Machine Learning for Healthcare Conference (MLHC) 2023

--------------------------------------------------------------------------------

Paper Submission Deadline:

Wednesday, April 12, 2023 11:59pm — Anywhere on Earth (AoE)

Conference dates:

August 11-12, 2023, Lerner Hall at Columbia University, New York, NY

Important Dates:

Paper Submission Deadline — Wednesday, April 12, 2023 11:59pm — Anywhere on
Earth (AoE)

Author Response Due — May 25, 2023

Acceptance Notification — June 21, 2023

Website:

https://www.mlforhc.org/

Submission Site:

https://cmt3.research.microsoft.com/MLHC2023

Call for Submissions:

For decades, researchers in computer science and medical informatics have
developed and applied machine learning techniques in the hope of leveraging
data to derive insights that could advance clinical medicine. Recently,
advances in machine learning (spanning theory, methods, and tooling) and
digital medicine (the advent of EHRs, public datasets, and technologically
minded clinicians) have created ideal conditions for leaps forward in
machine learning for healthcare. To realize this challenge, however, we
must tackle two grand challenges: (i) leveraging complex data (images,
other sensor data, and patient records consisting both raw and unstructured
data captured at irregular intervals); (ii) the need to provide actionable
insights (such as robust causal inferences about the likely impacts of
interventions). Moreover, realizing the potential of machine learning in
healthcare requires that technical researchers and clinicians work together
to identify the right problems, obtain the right data, and verify the
conclusions, and ultimately realize the potential of proposed solutions in
practice. While advances in deep learning have made a dent on the complex
data front, there’s far more work to be done. Meanwhile, the leap from
prediction to decision-making remains in its infancy. The Machine Learning
for Healthcare Conference (MLHC) is the premier publishing venue solely
dedicated to work at this vibrant intersection. MLHC has brought thousands
of machine learning and clinicians researchers together since its inception
to present groundbreaking work (archived in the Proceedings of Machine
Learning Research) and to forge new collaborations. We hope that you will
submit your strongest work to MLHC 2023 and will join us at Duke in August
for the conference.

Appropriate submissions include both (i) novel methods that tackle
fundamental problems arising in healthcare data (including sparsity,
multimodal data, class imbalance, temporal dynamics, distribution shift
across populations, and the need to estimate treatment effects); and (ii)
end-to-end machine learning solutions to important problems in healthcare
(including new methods, insightful evaluations of existing methods with
results of interest to the community, and in-vivo analyses of systems
deployed in the wild). We also welcome replication studies - please contact
the organizers prior to submission to ensure that your paper is within
scope and reviewed under the appropriate track. However, survey papers
which simply summarize existing methods will not be accepted. Submissions
will be reviewed by both computer scientists and clinicians. This year,
like previous years, we are calling for papers in two tracks: a research
paper track and a clinical abstract+software/demo track. Accepted papers
will be archived through the Proceedings of Machine Learning Research (JMLR
Proceedings track).

While it’s impossible to enumerate every conceivable problem of interest,
our guiding principle is that accepted papers should provide important new
generalizable insights about machine learning in the context of healthcare.

Submission Details

Research Track:

The review process is double blind. We expect papers to be between 10-15
pages (excluding references).  While there is no strict page limit, the
appropriateness of additional pages beyond the recommended length will be
judged by reviewers. Please refer to the submission instructions on our
website, including tips on what makes a great MLHC paper and required
content.  All papers will be rigorously peer-reviewed. Concerning dual
submissions, research that has previously been published, or is under
review, for an archival publication elsewhere may not be submitted. This
prohibition concerns only archival publications/submissions and does not
preclude papers accepted or submitted to non-archival workshops or
preprints (e.g., to arXiv). Accepted papers will be published through The
Proceedings of Machine Learning Research.

Clinical Abstract and Software/Demo Track:

In addition to our main research proceedings, we welcome the submission of
both (i) clinical abstracts; and (ii) software/demo abstracts, to be
presented in a non-archival track: Clinical abstracts typically pitch
clinical problems ripe for machine learning advances or describe
translational achievements.  The first author and presenter of a clinical
abstract track submission must be a clinician (often an MD or RN). Software
demos typically introduce a tool of interest to machine learning
researchers and/or clinicians in the community to use. These are often (but
not necessarily) open source tools.  Abstracts will not be archived.

Submissions Site:

https://cmt3.research.microsoft.com/MLHC2023

Program Chairs:

Rajesh Ranganath (NYU), Serena Yeung (Stanford University), Zachary Lipton
(Carnegie Mellon University), Shalmali Joshi (Columbia University),
Madalina Fiterau (University of Massachusetts Amherst)

--------------------------------------------------------------------------------

-- 
You received this message because you are subscribed to the Google Groups
"Machine Learning News" group.
To unsubscribe from this group and stop receiving emails from it, send an
email to ml-news+unsubscribe at googlegroups.com.
To view this discussion on the web visit
https://groups.google.com/d/msgid/ml-news/d102295e-0ee2-4649-8c4c-602d96f1db37n%40googlegroups.com
<https://groups.google.com/d/msgid/ml-news/d102295e-0ee2-4649-8c4c-602d96f1db37n%40googlegroups.com?utm_medium=email&utm_source=footer>
.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/autonlab-users/attachments/20230321/818db965/attachment.html>