[CFP] Machine Learning for Healthcare Conference (MLHC) 2024
Iñigo Urteaga
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Machine Learning for Healthcare Conference (MLHC) 2024
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Website:
Conference dates:
August 16 - 17, 2024, University of Toronto, Toronto, Canada
Important dates:
Paper Submission Deadline: April 9, 2024 11:59pm — Anywhere on Earth (AoE)
Author Response Due: May 24, 2024 11:59pm — Anywhere on Earth (AoE)
Acceptance Notification: June 21, 2024 11:59pm — Anywhere on Earth (AoE)
Submission Site:
https://openreview.net/group?id=mlforhc.org/MLHC/2024/Conference
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Call for Submissions:
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.
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.
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; (ii) End-to-end machine learning solutions and their integration into practice for important problems in healthcare; (iii) Sociotechnical and implementation science research; and (iv) Benchmark and reproducibility studies.
MLHC invites submissions to a full, archival Research Track and a non-archival Clinical Abstracts Track.
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Research Track Submission Details:
Please refer to the detailed submission instructions on our website, including tips on what makes a great MLHC paper and required content. All papers will be rigorously and double-blinded peer-reviewed. 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. 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). Accepted papers will be published through The Proceedings of Machine Learning Research.
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Clinical Abstract Track Submission Details:
In addition to our main research proceedings, we welcome the submission of clinical abstracts that pitch clinical problems ripe for machine learning advances or describe translational achievements. For instance (i) preliminary computational results; (ii) clinical/translational successes; (iii) open clinical questions or interesting data sets; (iv) demonstrations; (v) software. The first author and presenter of a clinical abstract track submission must be a clinician (often an MD or RN). Clinical abstracts will not be archived.
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Program Chairs:
Rajesh Ranganath (NYU), Zachary Lipton (Carnegie Mellon University), Madalina Fiterau (University of Massachusetts Amherst), Shalmali Joshi (Columbia University), Kaivalya Deshpande (Duke University), Iñigo Urteaga (Basque Center for Applied Mathematics)
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