Machine Learning for Health symposium 2024 -- Findings track

• URL: http://arxiv.org/abs/2503.00984v2
• Date: Fri, 11 Apr 2025 17:51:40 GMT
• Title: Machine Learning for Health symposium 2024 -- Findings track
• Authors: Stefan Hegselmann, Helen Zhou, Elizabeth Healey, Trenton Chang, Caleb Ellington, Vishwali Mhasawade, Sana Tonekaboni, Peniel Argaw, Haoran Zhang,
• Abstract summary: The 4th Machine Learning for Health symposium (ML4H 2024) was held on December 15-16, 2024, in Vancouver, BC, Canada.<n> ML4H 2024 invited high-quality submissions describing innovative research in a variety of health-related disciplines including healthcare, biomedicine, and public health.<n>The Proceedings track targeted mature, cohesive works with technical sophistication and high-impact relevance to health.<n>The Findings track promoted works that would spark new insights, collaborations, and discussions at ML4H.
• Score: 15.568227383838378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A collection of the accepted Findings papers that were presented at the 4th Machine Learning for Health symposium (ML4H 2024), which was held on December 15-16, 2024, in Vancouver, BC, Canada. ML4H 2024 invited high-quality submissions describing innovative research in a variety of health-related disciplines including healthcare, biomedicine, and public health. Works could be submitted to either the archival Proceedings track, or the non-archival Findings track. The Proceedings track targeted mature, cohesive works with technical sophistication and high-impact relevance to health. The Findings track promoted works that would spark new insights, collaborations, and discussions at ML4H. Both tracks were given the opportunity to share their work through the in-person poster session. All the manuscripts submitted to ML4H Symposium underwent a double-blind peer-review process.

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