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MLHC 2024 : 9th Machine Learning for Healthcare Conference


When Aug 16, 2024 - Aug 17, 2024
Where Toronto, Canada
Submission Deadline Apr 9, 2024
Notification Due May 24, 2024
Final Version Due Jun 28, 2024
Categories    machine learning   unbalanced data   generative ai   temporal dynamics

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. Please read the instructions about each track (below) carefully to determine which track best fits your work.

For decades, researchers in computer science and medical informatics have developed and applied machine learning techniques in the hope of leveraging data to derive insights that could advance clinical medicine. Recently, advances in machine learning (spanning theory, methods, and tooling) and digital medicine (the growth of EHRs, wearables, mobile health applications, public datasets, and technologically minded clinicians) have created ideal conditions for advances in machine learning for healthcare.

To realize this goal, however, we must tackle several challenges: (i) leveraging complex data (images, other sensor data, and patient records consisting both of raw and unstructured data captured at irregular intervals); (ii) the need to provide actionable insights (such as helping with decision-making and providing robust causal inferences about the likely impacts of interventions): and (iii) studying the clinical, social and technical interactions of ML models with healthcare stakeholder workflows, to understand the broader effects of ML and AI in healthcare.

Realizing the potential of machine learning in healthcare requires that technical researchers, clinicians, and social scientists work together to identify the right problems, curate the right data, and verify the conclusions, to ultimately realize the potential of proposed solutions in practice. While advances in deep learning have made a dent on the complex data front, there’s far more work to be done. Meanwhile, the leap from prediction to decision-making remains in its infancy.

The Machine Learning for Healthcare Conference (MLHC) is the premier publishing venue solely dedicated to work at this vibrant intersection. MLHC has brought thousands of machine learning and clinicians researchers together since its inception to present groundbreaking work (archived in the Proceedings of Machine Learning Research) and to forge new collaborations.

MLHC invites submissions to a full, archival Research Track and a non-archival Clinical Abstracts Track. The Research Track goes through a double-blind peer review, while submissions to the Clinical Abstracts Track are not-blinded during peer-review. Submissions accepted to both tracks will be presented at the proceedings event.

While it's impossible to enumerate every conceivable MLHC problem of interest, our guiding principle is that accepted papers should provide important new generalizable insights about machine learning in the context of healthcare. This year we are introducing several themes for submissions:

(i) Novel methods that tackle fundamental problems arising in healthcare data, including generative AI, sparsity, multimodal data, class imbalance, temporal dynamics, distribution shift across populations, fairness, and causal inference.

(ii) End-to-end machine learning solutions and their integration into practice for important problems in healthcare, including new ML solutions, insightful evaluations of existing methods with results of interest to the community, and in-vivo analyses of systems deployed in the wild.

(iii) Sociotechnical and implementation science research, including studies that measure clinical, operational, and economic impact; equity in community and broader effects of ML and AI in healthcare; as well as sociotechnical research that examines the social and technical interactions of ML models with healthcare stakeholders, including patients, clinicians, and organizational leaders.

(iv) Benchmark and reproducibility studies, including new datasets or replication studies —evaluation studies using previously proposed methods to assess whether results consistent with the original work can be obtained. Survey papers which simply summarize existing methods will not be accepted. Please contact the organizers prior to submission within this theme to ensure that your paper is within scope and reviewed under the appropriate track.
Research Track

MLHC research track submissions will be reviewed by both computer scientists and clinicians. Submissions under the sociotechnical and implementation science research theme will be reviewed by social scientists and clinical researchers with relevant expertise. Accepted papers will be archived through the Proceedings of Machine Learning Research (JMLR Proceedings track).

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. We call for submissions that describe the 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. 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).
Research Track Format

Please use the full paper LaTeX files available [here] for Research Track submissions.

The example paper in the file pack contains sample sections. The margins and author block must remain the same and all papers must be in 11-point Times font.

You must keep the generalizable insights heading in the introduction.

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.

We expect papers to be between 10-15 pages (excluding references). While there is no strict page limit, the appropriateness of additional pages beyond the recommended length will be judged by reviewers. Please refer to the submission instructions on our website, including tips on what makes a great MLHC paper and required content.

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.

Research Track REVIEW PROCESS:

All Research Track submissions 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 work 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)? For sociotechnical and implementation science submissions, are you serving as a social science reviewer?


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 PMLR 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.


Concerning dual submissions, research that has previously been published, or is under review, for an archival publication elsewhere may not be submitted. This prohibition concerns only archival publications/submissions and does not preclude papers accepted or submitted to non-archival workshops or preprints (e.g., to arXiv).

It is a violation of dual-submission policy to submit to another journal or conference a MLHC Research Track submission while under review at MLHC, or after its acceptance in the MLHC proceedings.
Clinical Abstract Track

In addition to our main Research Track proceedings, we welcome the submission of clinical abstracts to be presented in a non-archival, abstract track.

Clinical abstracts typically pitch clinical problems ripe for machine learning advances or describe translational achievements. The first author and presenter of a clinical abstract track submission must be a clinician (often an MD or RN).

The abstract may consist of:

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.

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.

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. Software demos typically introduce a tool of interest to machine learning researchers and/or clinicians in the community to use. These are often (but not necessarily) open source tools.

Abstracts will not be archived or indexed, but will have the opportunity to be presented as a poster and/or 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.
Clinical Abstract Track FORMAT:

Clinical Abstract Track submissions should be one page or less, using the abstract template [linked here].

Clinical abstracts are not blinded; author names, degrees, and affiliations should be present in the submission.
Clinical Abstract Track REVIEW PROCESS:

All clinical abstracts will be peer-reviewed by experts in the field.

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.

We expect one of the presenting authors to be a clinician. Please reach out to the organizers in case that is not possible.

Clinical Abstract Track 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).

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