Connectionists: MLHC: submission deadline extended to April 3

[Christian Shelton]

Whether it's transitioning to remote work or preparing to be/being at
the front lines, we recognize that now is a time of disruption for many
in our community. We are grateful for your efforts, and we have extended
the MLHC deadline to April 3. Full call below!


Call for Submissions:

MLHC -> Machine Learning for Healthcare Conference 2020

What: conference on data-driven healthcare

When: August 6-8, 2020*

Where: Durham, NC

Website: https://www.mlforhc.org/

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* At this time/given our conference is in late summer, we expect to
continue with the conference as planned, with options for those affected
by any continuing coronavirus travel restrictions to present remotely.

Researchers in machine learning --- including those working in statistical
natural language processing, computer vision, and related sub-fields ---
when coupled with seasoned clinicians can play an important role in turning
complex medical data (e.g., individual patient health records, genomic
data, data from wearable health monitors, online reviews of physicians,
medical imagery, etc.) into actionable knowledge that ultimately improves
patient care. For several years, MLHC has drawn together hundreds of
clinical and machine learning researchers to discuss machine learning
solutions clinicians need solved.

We invite submissions that advance our understanding of machine learning
in the context of healthcare.  Submissions may be methods oriented,
describing ways to address the challenges inherent to health-related
data (e.g., sparsity, class imbalance, causality, temporal dynamics,
multi-modal data).  They may also be more application-oriented, including
evaluations and analyses of state-of-the-art machine learning approaches
applied to health data in deployed/prototyped systems. Submissions will
be reviewed by both computer scientists and clinicians. This year 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).

Examples of topic areas include:

*Predicting individual patient outcomes
*Mining, processing and making sense of clinical notes
*Patient risk stratification
*Parsing biomedical literature
*Bio-marker discovery
*Brain imaging technologies and related models
*Learning from sparse/missing/imbalanced data
*Time series analysis with medical applications
*Medical imaging
*Efficient, scalable processing of clinical data
*Clustering and phenotype discovery
*Methods for vitals monitoring
*Feature selection/dimensionality reduction
*Text classification and mining for biomedical literature
*Exploiting and generating ontologies
*ML systems that assist with evidence-based medicine

Regardless of the topic, our main interest is in papers that teach us
something, that give us some new insights into machine learning in the
context of healthcare.

--- Submission Details ---

Research Track: Full papers are expected (in the range of 12-15 pages)
The review process is double blind. 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,
and research that has been previously published elsewhere or is currently
in submission may not be submitted. Accepted papers will be published
through the Proceedings of Machine Learning Research.

Clinical Abstract and Software/Demo Track: We also have a non-archival
track two very specific categories of papers: Clinical abstracts share open
clinical problems and celebrate translational achievements.  The first
author and presenter of a clinical abstract track submission must be an
MD/RN/clinician.  Software/demos share a tool for the community to use
(which generally means open source).  Abstracts will not be archived.

--- Important Dates ---

Paper Submission Deadline - Friday, April 3, 2020 6PM EDT

Acceptance Notification - Friday June 5th, 2020

Program Chairs:

Finale Doshi-Velez, PhD (Harvard University), James Fackler, MD (Johns
Hopkins), Kenneth Jung, PhD (Stanford University), David Kale (USC), Rajesh
Ranganath, PhD (NYU), Michael Sjoding, MD (University of Michigan), Byron
Wallace, PhD (Northeastern), Jenna Wiens, PhD (University of Michigan)