Machine Learning for Health symposium 2022 -- Extended Abstract track

• URL: http://arxiv.org/abs/2211.15564v1
• Date: Mon, 28 Nov 2022 17:15:22 GMT
• Title: Machine Learning for Health symposium 2022 -- Extended Abstract track
• Authors: Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy
• Abstract summary: 2nd Machine Learning for Health symposium (ML4H 2022) was held in New Orleans, Louisiana, USA. ML4H 2022 featured two submission tracks: a proceedings track and an extended abstract track. All manuscripts submitted to ML4H Symposium underwent a double-blind peer-review process.
• Score: 5.7582524698492925
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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