A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021
Fabian Falck, Yuyin Zhou, Emma Rocheteau, Liyue Shen, Luis Oala,, Girmaw Abebe, Subhrajit Roy, Stephen Pfohl, Emily Alsentzer, Matthew B. A., McDermott
TL;DR
This document compiles the accepted abstracts from the 2021 ML4H symposium, providing an overview of recent research in machine learning applications for health, though not all abstracts are included due to opt-outs.
Contribution
It offers a curated collection of recent ML4H research abstracts from 2021, serving as a resource for the community.
Findings
Provides an overview of accepted research topics in ML for health 2021
Highlights the diversity of approaches in the field
Serves as a reference for recent advancements
Abstract
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021. This index is not complete, as some accepted abstracts chose to opt-out of inclusion.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Healthcare · Machine Learning in Healthcare · Health, Environment, Cognitive Aging