Machine Learning for Health symposium 2022 -- Extended Abstract track

TL;DR

This collection presents innovative machine learning research related to health and biomedicine from ML4H 2022, highlighting emerging ideas and early-stage studies in the field.

Contribution

It showcases a variety of early-stage, innovative machine learning approaches for health, expanding the scope beyond mature, fully developed research.

Findings

Diverse innovative ML methods for health problems

Early-stage research discussions and feedback

Highlights of emerging trends in ML for health

Abstract

A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28, 2022, in New Orleans, Louisiana, USA. Machine Learning for Health (ML4H) is a longstanding venue for research into machine learning for health, including both theoretical works and applied works. ML4H 2022 featured two submission tracks: a proceedings track, which encompassed full-length submissions of technically mature and rigorous work, and an extended abstract track, which would accept less mature, but innovative research for discussion. All the manuscripts submitted to ML4H Symposium underwent a double-blind peer-review process. Extended abstracts included in this collection describe innovative machine learning research focused on relevant problems in health and biomedicine.