Machine Learning Models predicting Decompensation in Cirrhosis
Laboratory parameters, genetic variants and infections can help to predict the risk of cirrhosis decompensation. This proof-of-concept study adds data for the future development of advanced models to identify patients at risk.
Authors
- Sophie Elisabeth Müller
- Frank Lammert (ORCID: https://orcid.org/0000-0003-4450-7201)
- Marcin Krawczyk (ORCID: https://orcid.org/0000-0002-0113-0777)
- Alexander Zipprich (ORCID: https://orcid.org/0000-0001-8403-7983)
- Cristina Ripoll (ORCID: https://orcid.org/0000-0002-9704-4741)
- Matthias Reichert (ORCID: https://orcid.org/0000-0002-8192-0575)
- Paul M. Horn (ORCID: https://orcid.org/0000-0002-8755-7703)
- Markus Casper (ORCID: https://orcid.org/0000-0002-1163-8988)
Institutions
- Medizinische Hochschule Hannover (DE)
- Jena University Hospital (DE)
- Institute of Medical Microbiology and Hygiene (DE)
- University Hospital in Halle (DE)
- Charité - Universitätsmedizin Berlin (DE)
- Saarland University (DE)
Publication Details
- Journal
- Journal of Gastrointestinal and Liver Diseases
- Published
- 2025-03-27
- DOI
- https://doi.org/10.15403/jgld-5876
- Citations
- 5
- Primary Topic
- Liver Disease and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 6.61