Machine Learning for Healthcare
MLHC is an annual research meeting that exists to bring together two usually insular disciplines: computer scientists with artificial intelligence, machine learning, and big data expertise, and clinicians/medical researchers. MLHC supports the advancement of data analytics, knowledge discovery, and meaningful use of complex medical data by fostering collaborations and the exchange of ideas between members of these often completely separated communities. To pursue this goal, the event includes invited talks, poster presentations, panels, and ample time for thoughtful discussion and robust debate.
MLHC has a rigorous peer-review process and an (optional) archival proceedings through the Journal of Machine Learning Research proceedings track. You can access the inaugural proceedings here: http://proceedings.mlr.press/v68/
LIVE STREAM INFORMATION
We will be live streaming the main conference starting Friday morning via Zoom: go to https://stanford.zoom.us/j/941860335
New for 2018
- We now offer child care scholarships to facilitate the attendance of parents who need assistance with child care while attending the meeting. To apply for this scholarship, please follow the instructions on this form.
- This year we are pleased to offer tutorials on Machine Learning for clinicians, and The Health Care System for Engineers. The tutorials will be on Thursday afternoon, August 16 (the day before the conference). Please see the agenda for details.
Calls for Papers
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 the last seven years, this meeting has drawn hundreds of clinical and machine learning researchers to frame problems clinicians need solved and discuss machine learning solutions.
Deadline for submission: April 20, 2018 at 6pm (EDT)
Author notification: June 20, 2018
Tutorials: August 16, 2018
Conference: August 17-18, 2018
Li Ka Shing Learning and Knowledge Center
Stanford University School of Medicine
291 Campus Drive
Stanford, CA 94305