Mlforhc.org
2023 Accepted Papers — Machine Learning for Healthcare
WEBID 147: Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. Sharon Jiang, Zejiang Shen, Monica Agrawal, Barbara Lam, Nicholas Kurtzman, Steven Horng, David Karger, David Sontag. ID 150: A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization.
Actived: 4 days ago
Call for Papers — Machine Learning for Healthcare
WEBThe 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 …
2022 Conference — Machine Learning for Healthcare
WEBHealth[at]Scale - Topic: Recruiting and to discuss Health[at]Scale’s work. Optum - Topic: Optum Technology: Discussing our Company’s Work. Medical Informatics Corporation - Topic: Perioperative AI User Group. Note: Virtual Attendees can participate in “Virtual Roundtable Discussion 1” during this time at this link.
2023 Conference — Machine Learning for Healthcare
WEBDean of the Faculties of Health Sciences and the Vagelos College of Physicians and Surgeons (VP&S) 9:20 - 9:50 Ben Marlin, PhD. University of Massachusetts Amherst Manning College of Information and Computer Sciences “Uncertainty and Adaptive Interventions” 9:50 - 10:20 Doina Precup, PhD. McGill University School of Computer …
2019 Conference — Machine Learning for Healthcare
WEBData-Powered Women's Health: Endometriosis Characterization and Self-Management Abstract: Endometriosis is a chronic, inflammatory, and estrogen-dependent condition with a high burden on quality of life, estimated to affect 6-10% of women of reproductive age worldwide. Despite its high prevalence, it is an enigmatic condition: there is
2020 Conference — Machine Learning for Healthcare
WEBTitle: The Unpaved Path of Deploying Reliable and Human-Centered Machine Learning Systems. Abstract: As Machine Learning systems are increasingly becoming part of user-facing applications, their reliability and robustness are key to building and maintaining trust with users, especially for high-stake domains such as healthcare.
2021 Accepted Papers — Machine Learning for Healthcare
WEBStool Image Analysis for Precision Health Monitoring by Smart Toilets. Jin Zhou, Nick DeCapite, Jackson McNabb, Jose R. Ruiz, Deborah A. Fisher, Sonia Grego, Krishnendu Chakrabarty. Back to the basics with inclusion of clinical domain knowledge - A simple, scalable and effective model of Alzheimer’s Disease classification
2018 Conference — Machine Learning for Healthcare
WEB14:45-15:30 Joyce Lee, MD, MPH, University of Michigan. "When perfect algorithms meet Imperfect healthcare systems". Of all chronic diseases, diabetes is perhaps the one with the greatest opportunity for reaping the benefits of machine learning, given the role of patient management and the data-intensive nature of the condition.
How-to-write-a-great-MLHC-paper
WEBHOW TO WRITE A GREAT MLHC PAPER . Clinical Abstracts. Clinical abstracts must be written by a clinician first author who will present the work. Clinicians are broadly defined as those with a doctor of medicine (M.D.), nursing degree (R.N.), pharmacy degree (PharmD) or other allied health professional who spend a portion of the time involved in direct …
2024 Conference — Machine Learning for Healthcare
WEBBo Wang, PhD (Vector Institute, University Health Network. University of Toronto) 2024 Silver Sponsors. 2024 Bronze Sponsors. Back to Top. Any Questions? Contact us at: [email protected].
2021 Conference — Machine Learning for Healthcare
WEB15:15 - 16:00 Gold Sponsor Breakout Rooms: Breakout Room 1: HEALTH [at]SCALE: Transform Health Outcomes with Precision Care Delivery (Moderator: Kimis Perros) Access Zoom Link in Gather.Town Breakout Room 1! 16:00 - 17:30 Papers Research Track Posters B [ gather.town] Saturday August 7th, 2021, Virtual.
2017 Conference — Machine Learning for Healthcare
WEBConsider treatment for individuals struggling with chronic health conditions. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. That is, the treatment is adapted to the individual's context; the context may include current
2020 Accepted Papers — Machine Learning for Healthcare
WEBConflict of Interest Statement - Public trust in the peer review process and the credibility of published articles depend in part on how well conflict of interest is handled during writing, peer review, and editorial decision making. Conflict of interest exists when an author (or the author's institution), reviewer, or editor has financial or personal relationships that …
Organizers — Machine Learning for Healthcare
WEBKaivalya Deshpande, MD (NYU Langone Health) Iñigo Urteaga, PhD (BCAM) 2024 Local Organizing Committee. Rahul Krishnan, PhD (University of Toronto) Bo Wang, PhD (University of Toronto) Board of Directors. Finale Doshi, PhD (Associate Professor in Computer Science, Harvard School of Engineering and Applied Sciences)
2016 Conference — Machine Learning for Healthcare
WEBMachine Learning for Healthcare 2016 Saban Research Institute Program: 8:00 Breakfast. 8:45 Welcome. 9:00 Machine Learning Opportunities in the Explosion of Personalized Precision Medicine. Larry Smarr, PhD. We have reached the take off point in the generation of massive datasets from individuals and across populations, both of which are …
Registration — Machine Learning for Healthcare
WEBRegister for your early bird tickets now! Registration for MLHC 2024 includes access to all of the live talks, breakout sessions, poster sessions and includes meals for breakfast and lunch on Friday, August 16th and Saturday, August 17th.
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