{"id":145,"date":"2024-06-05T13:22:11","date_gmt":"2024-06-05T13:22:11","guid":{"rendered":"https:\/\/ahli.cc\/ml4h\/?page_id=145"},"modified":"2025-11-06T18:59:17","modified_gmt":"2025-11-06T18:59:17","slug":"call-for-papers","status":"publish","type":"page","link":"https:\/\/ahli.cc\/ml4h\/call-for-papers\/","title":{"rendered":"Call for Papers"},"content":{"rendered":"\n

ML4H 2025 invites submissions describing innovative research that lies in the broad purview of Machine Learning for Health. Authors are invited to submit work on relevant problems in a variety of health-related disciplines including healthcare, biomedicine, and public health. This year, ML4H 2025 will accept submissions for two distinct tracks: the Proceedings <\/em>track, for formal archival publications, and the non-archival Findings<\/em> track.<\/em><\/p>\n\n\n\n

In response to the growing ML4H community, ML4H has transitioned into a standalone symposium rather than a NeurIPS-affiliated workshop. This event represents a continuation of prior ML4H workshops\/symposiums (2016<\/a>, 2017<\/a>, 2018<\/a>, 2019<\/a>, 2020<\/a>, 2021<\/a>, 2022<\/a>, 2023<\/a>, 2024<\/a>) and will continue to be held in December directly before NeurIPS.<\/p>\n\n\n\n

If you are interested in being a reviewer, please reach out to us! We will be hosting a Reviewer Mentorship Program as well as Top Reviewer Awards. <\/p>\n\n\n\n

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Important Dates<\/h2>\n\n\n\n

Note: All times are in AoE<\/p>\n\n\n\n

Aug 1st: Submission site opens
Sep 8th<\/strong> 11:59 AoE<\/strong>: Paper submission deadline<\/s>
Oct 3rd : Reviews available; Author response period starts
Oct 16th: Author discussion period ends<\/s>
Oct\u00a027th: Decisions released<\/s>
Nov 3rd: Early Career Travel Grant Application
<\/s>Nov 11th: Camera-ready deadline
Dec 1-2nd: In-person event<\/p>\n\n\n\n

Quick Submission Instructions<\/h2>\n\n\n\n

Submission deadline has passed. <\/p>\n\n\n\n

ML4H 2025 LaTeX templates: <\/p>\n\n\n\n

Proceedings track: Overleaf<\/a><\/p>\n\n\n\n

Findings track: Overleaf<\/a><\/p>\n\n\n\n

The paper submission deadline is September 8th 11:59 PM AoE.<\/strong> Submissions must be fully anonymized<\/strong> and formatted using the ML4H 2025 LaTeX templates. Gross violations of formatting guidelines, malformed, non-blinded, non-health-related, or grossly insufficient works may be desk rejected by the organizing committee without undergoing additional review.<\/strong><\/p>\n\n\n\n

Submission Form<\/h2>\n\n\n\n

Data and Code: We encourage anonymized code and data submissions (if it can be made available with appropriate approval and guidelines) as supplemental material during review. If you are not sharing code, you must explicitly state this in the submission checklist. If your paper is accepted, we encourage public sharing of your code and\/or data for the camera-ready version of the paper.<\/p>\n\n\n\n

Ethics Board Approval: If your research requires IRB (or equivalent) approval or has been evaluated by your IRB as Not Human Subject Research, then for the camera-ready version of the paper, you must provide relevant information. At the time of submission for review, to preserve anonymity, it suffices to include a statement that relevant ethics approval information will be provided if the paper is accepted. If your research does not require IRB approval, please explicitly state this to be the case and provide a justification in the submission checklist.<\/p>\n\n\n\n

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Submission Tracks<\/h2>\n\n\n\n

ML4H 2025 will feature two main symposium submission tracks. <\/p>\n\n\n\n

Submissions to the main tracks will undergo double-blind peer review. Accepted submissions to all tracks will be featured at the event’s poster session. Accepted works for all tracks will be chosen based on their technical merit and contribution to the event. More details on how to write an excellent ML4H full paper or findings paper can be found here<\/a>. The salient differences between these tracks are described below. <\/p>\n\n\n\n

(A) Proceedings Track<\/h3>\n\n\n\n

Excellent ML4H Proceedings papers should be compelling, cohesive works with a high degree of technical sophistication as well as clear and high-impact relevance to health. Accepted proceedings papers will be published in the Proceedings for Machine Learning Research (PMLR). Full proceedings papers can be up to 8 pages at submission (excluding references and appendices). If your submission is accepted, you will be allowed 1 additional content page for the camera-ready version. <\/p>\n\n\n\n

Dual submission policy: Papers that are submitted to the ML4H proceedings track cannot be already published or under review in any other archival venue. Similarly, papers published to the ML4H proceedings may not be published again later at any other venue.<\/p>\n\n\n\n

(B) Findings Track<\/h3>\n\n\n\n

An excellent findings paper is one that leads to insights at the event through interaction with other attendees. This can be through presenting new ideas\/ways of thinking, leading to insightful discussion and feedback, dissemination of new valuable resources, or enabling new opportunities for collaborations. We also especially solicit \u201cnon-traditional research artifacts\u201d as submissions to the findings track, such as papers highlighting novel datasets, insightful negative results, exciting preliminary results that warrant rapid dissemination, reproducibility studies, and opinion pieces or critiques.<\/p>\n\n\n\n

Findings papers can be up to 4 pages at submission (excluding references and appendices), though additional information not critical for understanding the work can be included in an appendix without penalty (reviewers will review the work based predominantly on the main text). Findings papers will not appear in the ML4H proceedings, but upon acceptance, we invite (but do not require) authors to submit their findings (no page limit) to the ML4H arxiv.org index. <\/p>\n\n\n\n

Authors of accepted findings papers (non-archival submissions) retain full copyright of their work, and acceptance of such a submission to ML4H 2025 does not preclude publication of the same material in another archival venue (e.g. journal or conference). Furthermore, findings submissions that are under review or have been recently published (\u2264 6 months) in a conference or a journal are allowed; if this is the case, authors must ensure that they are not violating any other venue\u2019s dual submission policies. <\/p>\n\n\n\n

Track Switching<\/h2>\n\n\n\n

Proceedings submissions that are not accepted will automatically be considered for Findings. Findings track submissions cannot be considered for the Proceedings track. Decisions for both tracks will be released simultaneously.<\/p>\n\n\n\n

Reciprocal Reviewing Requirement<\/h2>\n\n\n\n

To support a high-quality and equitable review process, AHLI is introducing a new author reviewing policy based on submission volume. <\/p>\n\n\n\n

Every submission must nominate one author to review a minimum of three (3) papers.<\/strong> A qualified reviewer will have at least one prior archival publication at a comparable peer-reviewed venue (e.g. a ML for health conference or journal, or a health-focused paper at an ML venue). <\/p>\n\n\n\n

If none of the authors meet this qualification, the submission is exempt from this requirement. Please email info@ml4h.cc to request such an exemption. We welcome and encourage submissions from first-time contributors at ML4H. Authors serving as Area Chairs, Senior Area Chairs, or in other organizing roles for ML4H 2025 are exempt from this requirement. <\/p>\n\n\n\n

Authors of each submission must nominate at least one reciprocal reviewer at the time of submission. If an author is the nominated reciprocal reviewer for several papers, their reviewing load may increase accordingly. <\/p>\n\n\n\n

If the nominated reviewer does not accept their reviewer invitation, their associated submission will be desk rejected. Failure to adequately complete assigned reviews by the start of the author response period may also result in desk rejection of all associated submissions. <\/strong>Exceptions may be granted at the discretion of the Program Chairs. <\/p>\n\n\n\n

Submission Areas<\/h2>\n\n\n\n

Submitted papers should describe innovative machine learning research focused on relevant problems in health-related disciplines. Past works have spanned data integration, temporal models, deep learning, semi-supervised learning, reinforcement learning, transfer learning, few\/zero shot learning, learning from missing or biased data, learning from non-stationary data, causality, model biases, model evaluation, model criticism, model interpretability, model deployment, human-computer interaction, privacy\/security, and many more topics. General areas of interest include but are not limited to the following. (Note: these areas are non-exhaustive.)<\/strong> <\/p>\n\n\n\n

Area 1: Models and Methods: Algorithms, Inference, and Estimation<\/h3>\n\n\n\n
Description<\/h5>\n\n\n\n

Advances in machine learning are critical for a better understanding of health. This track seeks technical contributions in modeling, inference, and estimation in health-focused or health-inspired settings. We welcome submissions that develop novel methods and algorithms, introduce relevant machine learning tasks, identify challenges with prevalent approaches, or learn from multiple sources of data (e.g. non-clinical and clinical data).<\/p>\n\n\n\n

Our focus on health is broadly construed, including clinical healthcare, public health, and population health. While submissions should be primarily motivated by problems relevant to health, the contributions themselves are not required to be directly applied to real health data. For example, authors may use synthetic datasets to demonstrate properties of their proposed algorithms. <\/p>\n\n\n\n

We welcome submissions from many perspectives, including but not limited to supervised learning, unsupervised learning, reinforcement learning, causal inference, representation learning, survival analysis, domain adaptation or generalization, interpretability, robustness, and algorithmic fairness. All kinds of health-relevant data types are in scope, including tabular health records, time series, text, images, videos, knowledge graphs, and more. We welcome all kinds of methodologies, from deep learning to probabilistic modeling to rigorous theory and beyond.<\/p>\n\n\n\n

Example Papers <\/h5>\n\n\n\n

Kim V, Schneider L, Kalaie S, O\u2019Regan D, Bender C. \u201cHeartMAE: Advancing Cardiac MRI Analysis through Optical Flow Guided Masked Autoencoding<\/a>.\u201d Proceedings of the 4th Machine Learning for Health Symposium (ML4H), 2024.<\/p>\n\n\n\n

Khanna, S., Michael, D., Zitnik, M., Rajpurkar, P. \u201cLearning Generalized Medical Image Representations Through Image-Graph Contrastive Pretraining<\/a>.\u201d Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.<\/p>\n\n\n\n

Mao, H., Liu, H., Dou, J. X., Benos, P. V. \u201cTowards Cross-Modal Causal Structure and Representation Learning<\/a>.\u201d Proceedings of the 2nd Machine Learning for Health Symposium (ML4H), 2022.<\/p>\n\n\n\n

Moor, M., Huang, Q., Wu, S., Yasunaga, M., Dalmia, Y., Leskovec, J., Zakka, C., Reis, E. P., Rajpurkar, P. \u201cMed-Flamingo: A Multimodal Medical Few-Shot Learner<\/a>.\u201d Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.<\/p>\n\n\n\n

Wang, K. A., Fox, E. B. \u201cInterpretable Mechanistic Representations for Meal-Level Glycemic Control in the Wild<\/a>.\u201d Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.<\/p>\n\n\n\n

Tang, S., Dunnmon, J. A., Qu, L., Saab, K. K., Baykaner, T., Lee-Messer, C., Rubin, D. L. \u201cModeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models<\/a>.\u201d Proceedings of the Conference on Health, Inference, and Learning (CHIL), 2023.<\/p>\n\n\n\n

Shalit, U., Johansson, F.D., Sontag, D. “Estimating individual treatment effect: generalization bounds and algorithms<\/a>.” Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.<\/p>\n\n\n\n

Area 2: Applications and Practice: Investigation, Evaluation, Interpretation, and Deployment<\/h3>\n\n\n\n
Description<\/h5>\n\n\n\n

The goal of this track is to highlight works applying robust methods, models, or practices to identify, characterize, audit, evaluate, or benchmark ML approaches to healthcare problems. Additionally, we welcome unique deployments and datasets used to empirically evaluate these systems are necessary and important to advancing practice. Whereas the goal of Track 1 is to select papers that show significant algorithmic novelty, submit your work here if the contribution is describing an emerging or established innovative application of ML in healthcare. Areas of interest include but are not limited to:<\/p>\n\n\n\n