Use of machine learning for early prediction of short-term mortality in veterans with metabolic dysfunction-associated steatotic liver disease
Our data suggest that machine learning models based on data extracted from the EHR early during MASLD can identify patients likely to develop cirrhosis and predict short term mortality.
Authors
- Mulugeta Gebregziabher (ORCID: https://orcid.org/0000-0002-4826-481X)
- Wing‐Kin Syn (ORCID: https://orcid.org/0000-0001-7966-0500)
- L. Frey (ORCID: https://orcid.org/0000-0003-2816-4309)
- Yamini Natarajan (ORCID: https://orcid.org/0000-0002-8885-4507)
- Don C. Rockey (ORCID: https://orcid.org/0000-0002-3751-2961)
- Andrew D. Schreiner (ORCID: https://orcid.org/0000-0003-0914-3182)
- Ralph Ward (ORCID: https://orcid.org/0000-0001-6193-2838)
- Michael Fuchs (ORCID: https://orcid.org/0000-0002-8186-0779)
- Ahmad Basil Nasir (ORCID: https://orcid.org/0000-0003-1588-303X)
Institutions
- Medical University of South Carolina (US)
- University of the Basque Country (ES)
- Virginia Commonwealth University (US)
- University of Missouri–St. Louis (US)
- Kelsey-Seybold Clinic (US)
- Hunter Holmes McGuire VA Medical Center (US)
- Ralph H. Johnson VA Medical Center (US)
- Saint Louis University (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-10-27
- DOI
- https://doi.org/10.1371/journal.pone.0334715
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Veterans Affairs
- Health Services Research and Development
- National Institutes of Health