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Machine Learning for Healthcare Conference (MLHC) 2023 -------------------------------------------------------------------------------- Paper Submission Deadline: Wednesday, April 12, 2023 11:59pm — Anywhere on Earth (AoE) Conference dates: August 11-12, 2023, Lerner Hall at Columbia University, New York, NY Important Dates: Paper Submission Deadline — Wednesday, April 12, 2023 11:59pm — Anywhere on Earth (AoE) Author Response Due — May 25, 2023 Acceptance Notification — June 21, 2023 Website: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.mlforhc.org%2F&data=05%7C01%7Cuai%40engr.orst.edu%7C3492472e092d48332af808db2a705666%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638150434192375321%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=vQK5REUw03VD2tuBp1UmCuZbtvytUMrYJkhiplJSdtw%3D&reserved=0 Submission Site: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcmt3.research.microsoft.com%2FMLHC2023&data=05%7C01%7Cuai%40engr.orst.edu%7C3492472e092d48332af808db2a705666%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638150434192375321%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=PhQWbmS0dGxhExOxPY1429h5%2FsPKLb0pZVli0qZNMLk%3D&reserved=0 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 advent of EHRs, public datasets, and technologically minded clinicians) have created ideal conditions for leaps forward in machine learning for healthcare. To realize this challenge, however, we must tackle two grand challenges: (i) leveraging complex data (images, other sensor data, and patient records consisting both raw and unstructured data captured at irregular intervals); (ii) the need to provide actionable insights (such as robust causal inferences about the likely impacts of interventions). Moreover, realizing the potential of machine learning in healthcare requires that technical researchers and clinicians work together to identify the right problems, obtain the right data, and verify the conclusions, and 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. We hope that you will submit your strongest work to MLHC 2023 and will join us at Duke in August for the conference. Appropriate submissions include both (i) novel methods that tackle fundamental problems arising in healthcare data (including sparsity, multimodal data, class imbalance, temporal dynamics, distribution shift across populations, and the need to estimate treatment effects); and (ii) end-to-end machine learning solutions to important problems in healthcare (including new methods, insightful evaluations of existing methods with results of interest to the community, and in-vivo analyses of systems deployed in the wild). We also welcome replication studies - please contact the organizers prior to submission to ensure that your paper is within scope and reviewed under the appropriate track. However, survey papers which simply summarize existing methods will not be accepted. Submissions will be reviewed by both computer scientists and clinicians. This year, like previous years, we are calling for papers in two tracks: a research paper track and a clinical abstract+software/demo track. Accepted papers will be archived through the Proceedings of Machine Learning Research (JMLR Proceedings track). While it’s impossible to enumerate every conceivable problem of interest, our guiding principle is that accepted papers should provide important new generalizable insights about machine learning in the context of healthcare. Submission Details Research Track: The review process is double blind. 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. All papers will be rigorously peer-reviewed. 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. Clinical Abstract and Software/Demo Track: In addition to our main research proceedings, we welcome the submission of both (i) clinical abstracts; and (ii) software/demo abstracts, to be presented in a non-archival 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). 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. Submissions Site: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcmt3.research.microsoft.com%2FMLHC2023&data=05%7C01%7Cuai%40engr.orst.edu%7C3492472e092d48332af808db2a705666%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638150434192375321%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=PhQWbmS0dGxhExOxPY1429h5%2FsPKLb0pZVli0qZNMLk%3D&reserved=0 Program Chairs: Rajesh Ranganath (NYU), Serena Yeung (Stanford University), Zachary Lipton (Carnegie Mellon University), Shalmali Joshi (Columbia University), Madalina Fiterau (University of Massachusetts Amherst) --------------------------------------------------------------------------------
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