Call for Submissions MLHC -> Machine Learning for Healthcare Conference 2020
What: conference on data-driven healthcare When: August 6-8, 2020 Where: Durham, NC Website: https://www.mlforhc.org/ ------------------------------------------------------------------- Researchers in machine learning --- including those working in statistical natural language processing, computer vision, and related sub-fields --- when coupled with seasoned clinicians can play an important role in turning complex medical data (e.g., individual patient health records, genomic data, data from wearable health monitors, online reviews of physicians, medical imagery, etc.) into actionable knowledge that ultimately improves patient care. For several years, MLHC has drawn together hundreds of clinical and machine learning researchers to discuss machine learning solutions clinicians need solved. 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. Submissions will be reviewed by both computer scientists and clinicians. This year 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). Examples of topic areas include: *Predicting individual patient outcomes *Mining, processing and making sense of clinical notes *Patient risk stratification *Parsing biomedical literature *Bio-marker discovery *Brain imaging technologies and related models *Learning from sparse/missing/imbalanced data *Time series analysis with medical applications *Medical imaging *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 Regardless of the topic, our main interest is in papers that teach us something, that give us some new insights into machine learning in the context of healthcare. --- Submission Details --- Research Track: Full papers are expected (in the range of 12-15 pages) The review process is double blind. 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, and research that has been previously published elsewhere or is currently in submission may not be submitted. Accepted papers will be published through the Proceedings of Machine Learning Research. Clinical Abstract and Software/Demo Track: We also have a non-archival track two very specific categories of papers: Clinical abstracts share open clinical problems and celebrate translational achievements. The first author and presenter of a clinical abstract track submission must be an MD/RN/clinician. Software/demos share a tool for the community to use (which generally means open source). Abstracts will not be archived. --- Important Dates --- Paper Submission Deadline - Friday March 20th, 2020 6PM EDT Acceptance Notification - Friday June 5th, 2020 Program Chairs: Finale Doshi, PhD (Harvard University), James Fackler, MD (Johns Hopkins), Kenneth Jung, PhD (Stanford University), David Kale (USC), Rajesh Ranganath, PhD (NYU), Michael Sjoding, MD (University of Michigan), Byron Wallace, PhD (Northeastern), Jenna Wiens, PhD (University of Michigan) _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
