Fri Dec 08 08:00 AM -- 06:30 PM (PST) @ 104 A
Machine Learning for Health (ML4H) - What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now?
The goal of the NIPS 2017 Machine Learning for Health Workshop (ML4H) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. We aim to build on the success of the last two NIPS ML4H workshops which were widely attended and helped form the foundations of a new research community.
This year’s program emphasizes identifying previously unidentified problems in healthcare that the machine learning community hasn't addressed, or seeing old challenges through a new lens. While healthcare and medicine are often touted as prime examples for disruption by AI and machine learning, there has been vanishingly little evidence of this disruption to date. To interested parties who are outside of the medical establishment (e.g. machine learning researchers), the healthcare system can appear byzantine and impenetrable, which results in a high barrier to entry. In this workshop, we hope to reduce this activation energy by bringing together leaders at the forefront of both machine learning and healthcare for a dialog on areas of medicine that have immediate opportunities for machine learning. Attendees at this workshop will quickly gain an understanding of the key problems that are unique to healthcare and how machine learning can be applied to addressed these challenges.
The workshop will feature invited talks from leading voices in both medicine and machine learning. A key part of our workshop is the clinician pitch; a short presentation of open clinical problems where data-driven solutions can make an immediate difference. This year’s program will also include spotlight presentations and two poster sessions highlighting novel research contributions at the intersection of machine learning and healthcare. The workshop will conclude with an interactive a panel discussion where all speakers respond to questions provided by the audience.