Machine Learning for Health symposium 2023 -- Findings track

• URL: http://arxiv.org/abs/2312.00655v3
• Date: Fri, 15 Dec 2023 17:10:35 GMT
• Title: Machine Learning for Health symposium 2023 -- Findings track
• Authors: Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Thomas Hartvigsen, Harvineet Singh
• Abstract summary: ML4H 2023 invited high-quality submissions on relevant problems in a variety of health-related disciplines. Papers were targeted at mature work with strong technical sophistication and a high impact to health. The Findings track looked for new ideas that could spark insightful discussion, serve as valuable resources for the community, or could enable new collaborations.
• Score: 16.654806183414976
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A collection of the accepted Findings papers that were presented at the 3rd Machine Learning for Health symposium (ML4H 2023), which was held on December 10, 2023, in New Orleans, Louisiana, USA. ML4H 2023 invited high-quality submissions on relevant problems in a variety of health-related disciplines including healthcare, biomedicine, and public health. Two submission tracks were offered: the archival Proceedings track, and the non-archival Findings track. Proceedings were targeted at mature work with strong technical sophistication and a high impact to health. The Findings track looked for new ideas that could spark insightful discussion, serve as valuable resources for the community, or could enable new collaborations. Submissions to the Proceedings track, if not accepted, were automatically considered for the Findings track. All the manuscripts submitted to ML4H Symposium underwent a double-blind peer-review process.

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