Development and external validation of a machine learning model to predict the initial dose of vancomycin for targeting an area under the concentration–time curve of 400–600 mg∙h/L
The proposed model is capable of accurately predicting the optimal dose of vancomycin, which could aid physicians in making more informed treatment decisions regarding early therapy, particularly when AUC estimation systems are not accessible or readily available in routine clinical practice.
Authors
- Ji-Hun Kim (ORCID: https://orcid.org/0000-0002-4974-4129)
- Yun Woo Lee (ORCID: https://orcid.org/0000-0001-6452-4020)
- Hyeonji Seo (ORCID: https://orcid.org/0000-0001-6659-6853)
- Yong Kyun Kim (ORCID: https://orcid.org/0000-0002-7935-6952)
- H. J. Park (ORCID: https://orcid.org/0000-0002-5326-9711)
- Jin Ju Park
Institutions
- Hallym University (KR)
- Sacred Heart Hospital (NG)
- Sacred Heart Hospital (US)
- Hallym University Kangnam Sacred Heart Hospital (KR)
- The Catholic University of Korea Uijeongbu St. Mary's Hospital (KR)
- Hallym University Sacred Heart Hospital (KR)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.105817
- Citations
- 2
- Primary Topic
- Antimicrobial Resistance in Staphylococcus
- Type
- article
- Field-Weighted Citation Impact
- 2.79
Funders
- Ministry of Science and ICT, South Korea