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
- 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
Research Track 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. However, authors will have the option of only archiving the abstract to allow for future submissions to clinical journals, etc.
Research Track Submission Details. We do not have a page limit for submissions but submissions should typically fit into 12-15 pages (including references). The review process is double blind. Please refer to the submission instructions below.
There is no maximum paper length. Supplementary materials can be uploaded separately. We expect papers to be between 12-15 pages (including references); 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
MLHC Style File 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 that describe your problem, cohort, features used, methods, results, etc. There is no limit on the length of your submission, but most submissions fit into 12-15 pages (including references). 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!)
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.
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:
- You may submit a paper to MLHC and a journal at the same time.
- You may submit work that has only appeared at a conference or workshop without proceedings.
- 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.
Please upload submissions here: https://cmt3.research.microsoft.com/MLHC2018
Clinical Abstract Track
To expose open questions and celebrate the accomplishments of the community, we are also invite submissions for late-breaking clinical podium abstracts and demos:
- Open clinical questions: we seek viewpoints from clinicians and clinical researchers on important directions the MLHC community should tackle together.
- Clinical/translational successes: we seek abstracts about data and data analysis that resulted in new understanding and/or changes in clinical practice.
- Demonstrations: we seek exciting end-to-end tools that bring data and data analysis to the clinician/bedside.
We especially encourage submissions from clinical researchers working with large digital health data sets using modern computational methods. Submissions should be one page or less, and accepted submissions will presented as late-breaking abstracts and demos at MLHC. Abstracts will be made available online, but will not be archived or indexed.
Proceedings and Review Process: Accepted submissions will be published online. All clinical abstracts will be peer-reviewed.
Clinical Abstract Track Submission Details.: Clinical abstracts are not blinded; author names should be present in the submission. All clinical abstracts will be peer-reviewed. Accepted submissions will be published online. Please upload submissions here: https://cmt3.research.microsoft.com/MLHC2018