Machine learning-based prediction model for teicoplanin plasma concentrations in adults with liver disease using real-world data
The LightGBM-based model integrating clinical covariates demonstrated robust predictive capability for TEIC plasma concentrations in liver disease. This tool provides real-world evidence to optimize TEIC dosing, advancing individualized treatment strategies to improve therapeutic outcomes in this population.
Authors
- Feng Jian (ORCID: https://orcid.org/0000-0003-1199-7048)
- Ru‐Rong Ji (ORCID: https://orcid.org/0000-0002-9355-3688)
- Huang Jian (ORCID: https://orcid.org/0000-0002-6114-5815)
- Ming X. Wang (ORCID: https://orcid.org/0000-0001-5528-0773)
- Ze Yu (ORCID: https://orcid.org/0000-0002-4727-8427)
- Liying Liang
- Xiaodong Chen
- Xuechun Li (ORCID: https://orcid.org/0009-0004-5067-4553)
- Yanfang Chen (ORCID: https://orcid.org/0000-0002-6182-5036)
- Zhihao Guo (ORCID: https://orcid.org/0009-0003-5146-2424)
Institutions
- Guangzhou Eighth People's Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Pharmacology
- Published
- 2025-12-05
- DOI
- https://doi.org/10.3389/fphar.2025.1703976
- Citations
- 1
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Basic and Applied Basic Research Foundation of Guangdong Province