Machine Learning for Health symposium 2024 -- Findings track
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917982809096192 |
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| author | Hegselmann, Stefan Zhou, Helen Healey, Elizabeth Chang, Trenton Ellington, Caleb Mhasawade, Vishwali Tonekaboni, Sana Argaw, Peniel Zhang, Haoran |
| author_facet | Hegselmann, Stefan Zhou, Helen Healey, Elizabeth Chang, Trenton Ellington, Caleb Mhasawade, Vishwali Tonekaboni, Sana Argaw, Peniel Zhang, Haoran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Health symposium 2024 -- Findings track Hegselmann, Stefan Zhou, Helen Healey, Elizabeth Chang, Trenton Ellington, Caleb Mhasawade, Vishwali Tonekaboni, Sana Argaw, Peniel Zhang, Haoran Machine Learning 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. |
| title | Machine Learning for Health symposium 2024 -- Findings track |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.00984 |