Deadline for submission: March 29th, 2019 AoE
Author notification: June 7th, 2019
Please upload submissions here: https://cmt3.research.microsoft.com/MLHC2019
MLHC has two tracks a research track and a clinical abstract track. Please read the instructions carefully before uploading your submission.
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
Brain imaging technologies and related models
Learning from sparse/missing/imbalanced data
Time series analysis with medical applications
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 of Machine Learning Research (formerly the JMLR Workshop and Proceedings Track) and indexed on PubMed. All papers will be rigorously peer-reviewed, and research that has been previously published elsewhere or is currently in submission may not be submitted.
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.
Research Track - Submission Instructions
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.
MLHC Style Files are 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). It is a violation of dual-submission policy to publish at MLHC and then later submit the same paper to another conference. 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!)
Research Track - Sections
Before you get started, we encourage you read the content on “How to Write a Great MLHC Paper.” 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.
Research Track - 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.
Research Track - 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 (assuming you follow the journal’s rules).
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 the research track of MLHC must be full papers so that the work can be rigorously reviewed.
Clinical Abstract Track
The clinical abstract is designed for clinical researchers who wish to share open questions and accomplishments with the community in a shorter, non-archival form. The abstract may consist of:
Open clinical questions or interesting data sets: we seek viewpoints from clinicians and clinical researchers on important directions the MLHC community should tackle together, as well as abstracts describing interesting data sources.
Preliminary computational results: we encourage submissions from clinical researchers working with digital health data using modern computational methods; MLHC is a great venue for clinical researchers to brainstorm further analyses with an engaged computational community.
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.
Submissions should be one page or less. Abstracts will not be archived or indexed, but will have the opportunity to be presented as a poster and spotlight talk at MLHC. Given that this track is designed to engage clinicians, the first author of a clinical abstract must be a clinician (MD, RN, etc. -> does your job involve working with patients). We also expect that the clinician will present the work (perhaps in a team). The clinical abstract track is not intended for work-in-progress by primarily computational researchers.
Please use the following formatting - template here
Clinical Abstract Track - Proceedings and Review Process: All clinical abstracts will be peer-reviewed. They will not be archived.
Clinical Abstract Track - Submission Details.: Clinical abstracts are not blinded; author names and degrees should be present in the submission. All clinical abstracts will be peer-reviewed.