SAVE THE DATE Machine Learning in Health Care


August 18th - 19th, 2017
Northeastern University, Boston, MA

Find Out More

Machine Learning in Health Care


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.

MLHC has a rigorous peer-review process and an (optional) archival proceedings through the Journal of Machine Learning Research proceedings track. You can access the inaugural proceedings here: http://www.jmlr.org/proceedings/papers/v56/

Important Dates


  • Submission Deadline: Monday April 24th at 6pm (EDT)
  • Author Notification: Friday June 16th
  • Conference: Aug 18th - 19th, 2017

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 which is indexed through Pubmed.

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
  • Integration of clinical, omics, social media, and mobile sources
  • Public health and pharmaco-surveillance

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 11-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.

Context for Clinicians: We realize that conferences in medicine tend to be abstract-only, non-archival events. This is not the case for MLHC: to be a premier health and machine learning venue, all papers submitted to MLHC will be rigorously peer-reviewed for scientific quality -- and for that a suitably complete description of the work is necessary. So we call for submissions of 7-10 pages that describe your problem, cohort, features used, methods, results, etc. Multiple reviewers will provide feedback on the submission. If accepted, you will have the opportunity to revise the paper before submitting the final version.

Context for Computer Scientists: MLHC is a machine learning conference, and we expect papers of the same level of quality as those that would be sent to a conference (rather than a workshop). One may choose to only have the abstract of the paper archived, but it is a violation of dual-submission policy to archive the full MLHC paper and then later submit the same paper to another conference

Regardless of whether or not the full paper is archived, authors of accepted papers will be invited to present a spotlight and/or a poster on their work at the conference.

(Of course, we hope that many papers have both clinicians and computer scientists involved!)

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


We will be updating the Program Section as it becomes available.

Need more information?


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