MLHC 2021 : Machine Learning for Healthcare
Call For Papers
MLHC has two tracks a research track, for full papers to be archived, and a clinical abstract track, non-archival short papers highlighting work by clinicians as well as software and demos.
Research Track: Call for Papers, Review Process
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. We seek high-quality submissions on wide range of topics, including:
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
Additional 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. If you wish to submit a shorter, non-archival paper, see the Clinical Abstracts track below.
Additional 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).
Please use the full paper LaTeX files in the file pack above. The example paper in the file pack 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. Moreover, you must keep the the generalizable insights heading in the introduction. The margins and author block must remain the same and all papers must be in 11-point Times font.
There is no maximum paper length. Supplementary materials can be uploaded separately. If supplementary materials are included, the paper must still stand alone; reviewers are encouraged but not required to look at the supplementary materials. We expect papers to be between 12-15 pages (including references); shorter papers are acceptable as long as they fully describe the work.
Finally, papers must be submitted blinded. 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.
All papers will be rigorously peer-reviewed by both clinicians and ML researchers, with an emphasis on what generalizable insights the work provides about machine learning in the context of healthcare. We encourage you to read the content on “How to Write a Great MLHC Paper.” to see what we are looking for as well as common pitfalls. Reviewing for MLHC is double-blind: the reviewers will not know the authors’ identity and the authors will not know the reviewers’ identity (see format section above for more notes).
Finally, below are the questions on the review form, so that you know exactly what reviewers will be asked about your paper:
Summarize the paper and its main contributions
What generalizable insights did the authors claim they are making to machine learning in the context of healthcare?
Were the claims of these insights supported in the body of the paper?
Please provide detailed comments, including strengths and weaknesses of the paper.
Is your main expertise on the clinical or computational side (or both)?
Proceedings and presentations:
Accepted submissions will be published through the Proceedings of Machine Learning Research (formerly the JMLR Workshop and Proceedings Track) and we are in the process of securing indexing on PubMed. Publications through JMLR are made open access without an article processing fee. Authors of accepted papers will be invited to present a spotlight and/or a poster on their work at the conference.
It is a violation of dual-submission policy to publish at MLHC and then later submit the same paper to another conference.
Dual Submission 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).
Clinical Abstract Track
call for abstracts:
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, as well as highlight demonstrations of tools and sofware. 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.
Software: we seek abstracts describing processing tools/pipelines tailored to health data.
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) or a clinician-in-training (i.e., currently enrolled in an MD or MD/PhD program). The clinical abstract track is not intended for work-in-progress by primarily computational researchers.
Submissions should be one page or less, using the abstract template from the file pack above. Clinical abstracts are not blinded; author names, degrees, and affiliations should be present in the submission.
All clinical abstracts will be peer-reviewed.
Proceedings and presentations:
Abstracts will not be archived. Authors of accepted papers will be invited to present a spotlight and/or a poster on their work at the conference. One of the presenting authors must be a clinician.
dual submission policy:
Work in progress, work in submission, and recently published work are all welcome (as long as you follow the other publication’s rules).