An empirical study of using radiology reports and images to improve intensive care unit mortality prediction
The deep learning model demonstrated superior performance to traditional machine learning methods under the same feature fusion setting for ICU mortality prediction. This study highlights the potential of integrating multimodal data into deep learning models to enhance the accuracy of ICU mortality prediction.
Authors
- Mingquan Lin (ORCID: https://orcid.org/0000-0003-0862-6588)
- Ying Ding (ORCID: https://orcid.org/0000-0003-2567-2009)
- Lihui Zhao (ORCID: https://orcid.org/0000-0002-6877-9490)
- Yifan Peng (ORCID: https://orcid.org/0000-0001-9309-8331)
- Song Wang (ORCID: https://orcid.org/0000-0002-8224-0424)
- Fei Wang (ORCID: https://orcid.org/0000-0001-6344-1872)
Institutions
- Northwestern University (US)
- University of Minnesota (US)
- Cornell University (US)
- The University of Texas at Austin (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/jamiaopen/ooae137
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Science Foundation
- National Institutes of Health
- U.S. National Library of Medicine