MUCMD Machine Learning in Health Care


August 19th - 20th, 2016
Los Angeles, CA

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Machine Learning in Health Care


MUCMD is being renamed as Machine Learning in Health Care (MLHC). MLHC is an annual research meeting that exists to bring together two usually insular disciplines: computer scientists with artificial intelligence, machine learning, and big data expertises, with clinicians , and medical researchers. MLHC supports the advancement of data analytics, knowledge discovery, and seriously meaningful use of complex medical data by fostering collaborations and the exchange of ideas between members of these too often completely separated communities. To this end, the symposium includes invited talks, poster presentations, panels, and ample time for thoughtful discussion and robust debate.

We are also pleased to announce that, for the first time, MLHC will be introducing a rigorous peer-review process and (optional) archival proceedings through the Journal of Machine Learning Research proceedings track.

Important Dates


  • Paper Submission - May 15, 2016 11:59 PM PDT
  • Acceptance Notification - June 15, 2016
  • Conference: August 19-20, 2016

Call for Papers


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 the last six years, MUCMD has drawn about 100 clinical and machine learning researchers to frame problems clinicians need solved and discuss machine learning solutions; this year we are introducing a rigorous review process which will include both computer scientists and clinicians. Accepted papers will be (optionally) archived through the Journal of Machine Learning Research proceedings track.

We invite submissions that describe novel methods to address the challenges inherent to health-related data (e.g., sparsity, class imbalance, causality, temporal dynamics, multi-modal data). We also invite articles describing the application and evaluation of state-of-the-art machine learning approaches applied to health data in deployed systems. In particular, we seek high-quality submissions on the following topics:

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

Proceedings and Review Process. Accepted submissions will be published through the proceedings track of the Journal of Machine Learning Research. All papers will be rigorously peer-reviewed, and research that has been previously published elsewhere or is currently in submission may not be submitted to MLHC. However, authors will have the option of only archiving the abstract to allow for future submissions to clinical journals, etc.

Submissions


The maximum paper length is 10 pages, excluding references, acknowledgements, and supplementary materials. The maximum size is 10 MB. We expect papers to be between 7-10 pages; shorter papers are acceptable as long as they fully describe the work.

Here is an example paper

LaTeX style files are available here

A Word template is available here

While section headings may be changed, the margins and author block must remain the same and all papers must be in 10-point Times font. If supplementary materials are included, the paper must still stand alone; reviewers are encouraged but not required to look at the supplementary materials.

Sections

The example paper contains sample sections. A more machine-learning oriented paper may include more mathematical details, while a more application-focused paper may include more detailed cohort and study design descriptions. In all cases, papers should contain enough information for the readers to understand and reproduce the results.

Double-Blind Reviewing

Reviewing for MLHC is double-blind: the reviewers will not know the authors’ identity and the authors will not know the reviewers’ identity. Do not include your names, your institution’s name, or identifying information in the initial submission. Wait for the camera-ready. While you should make every effort to anonymize your work -- e.g. write “In Doe et al. (2011), the authors…” rather than “In our previous work (Doe et al., 2011), we…” -- we realize that a reviewer may be able to deduce the authors’ identities based on the previous publications or technical reports on the web. This will not be considered a violation of the double-blind reviewing policy on the author’s part.

Dual Submission and Archiving Policy

All submissions to MLHC must be novel work. You may not submit work that has been previously published, accepted for publication, or that has been submitted in parallel to other conferences. There are a few exceptions:

  1. You may submit a paper to MLHC and a journal at the same time.
  2. You may submit work that has only appeared at a conference or workshop without proceedings.
  3. You may submit work that has only been previously published as a technical report (e.g. on arXiv).

All submissions to MLHC must be full papers so that the work can be rigorously reviewed. Once your paper is accepted to MLHC, however, you may choose to only have the abstract archived to enable submission to a journal.

Program


Invited Speakers

Clinical Assitant Professor, Anesthesiology, Perioperative and Pain Medicine Stanford Medicine
Professor of Computer Science and Information Technologies University of California, San Diego
Assistant Professor, Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai
Professor of Electrical & Computer Engineering Duke University
Professor of Electrical Engineering and Computer Science Massachusetts Institute of Technology
Associate Professor, School of Computer Science McGill University
Professor of Medicine, Biomedical Engineering and Molecular Physiology and Biological Physics University of Virgina
Assistant Professor Medicine Beth Israel Deaconess Medical Center
Professor of Critical Care Medicine University of Pittsburgh
Senior Systems Scientist, Robotics Institute Carnegie Mellon University

Program Chairs

Assistant Professor in Computer Science, Harvard School of Engineering and Applied Sciences
Associate Professor Departments of Anesthesiology/Critical Care Medicine and Pediatrics Johns Hopkins University School of Medicine
PhD Student, Computer Science, Viterbi Dean's Doctoral Fellow, and Alfred E. Mann Innovation in Engineering Fellow at the University of Southern California
Assistant professor at the University of Texas at Austin
Assistant Professor of Computer Science and Engineering (CSE) at the University of Michigan

Senior Advisory Committee:

Dean of the College of Computer and Information Science, Northeastern University
Associate Professor and Canada Research Chair in Computational Biology, University of Toronto
Associate Professor, Biomedical Informatics Emory University
Associate Professor of Biomedical Informatics, Affiliated with Computer Science, Columbia University
Professor of Computer Science at Cornell Tech in New York City and a Professor of Public Health at Weill Cornell Medical College
Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at The University of Texas at Austin
Professor of Computer Science at the University of Alberta
Dugald C. Jackson Professor MIT Department of Electrical Engineering and Computer Science
Professor of Computer Science, University of Pittsburgh
Technical Fellow and Managing Director, Microsoft Research
Lawrence J. Henderson Professor of Pediatrics, Boston Childrens Hospital
HST Faculty, Distinguished Professor in Health Sciences and Technology and Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Professor of Medicine, Biomedical Engineering and Molecular Physiology and Biological Physics
Professor of Computer Science at the University of British Columbia
Senior Lecturer in Computer Science at Makerere University
Associate Professor at UC Riverside's Computer Science Department
Professor of Computer Science and Engineering in the MIT Department of Electrical Engineering and Computer Science
Associate Professor, Medicine - Biomedical Informatics Research, Stanford University
Founder’s Board Chair of Neurocritical Care, Professor in Pediatrics-Neurology, Neurology - Ken and Ruth Davee Department and Pharmacology, Northwestern
Chairman, Department of Anesthesiology Critical Care Medicine - Children's Hospital Los Angeles
Professor of Machine Learning, School of Informatics, University of Edinburgh

Need more information?


If you have any questions regarding the symposium, please send us an email.