A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records
The TECO model demonstrates a strong capability for predicting ICU mortality using continuous monitoring data. While further validation is needed, TECO has the potential to serve as a powerful early warning tool across various diseases in inpatient settings.
Authors
- Ruichen Rong (ORCID: https://orcid.org/0000-0002-3205-8915)
- Guanghua Xiao (ORCID: https://orcid.org/0000-0001-9387-9883)
- Donghan M. Yang (ORCID: https://orcid.org/0000-0003-1935-0214)
- Clark Walker
- Ferdinand Velasco
- Ann Marie Návar (ORCID: https://orcid.org/0000-0002-6197-9860)
- Kevin W. Jin (ORCID: https://orcid.org/0000-0002-9217-4803)
- Tanna L. Nelson
- Zifan Gu (ORCID: https://orcid.org/0000-0002-8024-2629)
- Eric David Peterson (ORCID: https://orcid.org/0000-0002-5415-4721)
- Tony Keller
- Hongyin Lai (ORCID: https://orcid.org/0000-0002-8613-5920)
- Yang Xie
- Catherine Chen (ORCID: https://orcid.org/0000-0002-3367-0655)
Institutions
- Yale University (US)
- Southwestern Medical Center (US)
- Texas Health Resources Foundation (US)
- The University of Texas Southwestern Medical Center (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf026
- Citations
- 8
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 19.85
Funders
- Cancer Prevention and Research Institute of Texas
- National Institutes of Health