Machine learning approach for dosage individualization of azithromycin in children with community‐acquired pneumonia
ML models were established to predict the AUC<sub>0-24</sub> of azithromycin successfully and could be used for individual dose adjustment in children before treatment and after obtaining C<sub>0</sub>.
Authors
- Guo‐Xiang Hao (ORCID: https://orcid.org/0000-0003-4847-091X)
- John van den Anker (ORCID: https://orcid.org/0000-0003-0571-7492)
- Bo‐Hao Tang (ORCID: https://orcid.org/0009-0000-8266-916X)
- X. Y. Zhang
- Wei Zhang (ORCID: https://orcid.org/0000-0001-9459-680X)
- Yue Zhou (ORCID: https://orcid.org/0000-0002-1335-5703)
- Yi Zheng (ORCID: https://orcid.org/0000-0003-0193-1213)
- Wei Zhao (ORCID: https://orcid.org/0000-0002-1830-338X)
- Li‐Yuan Tian
- Yue‐E Wu
- Shu‐Meng Fu
- Bu‐Fan Yao
- Yakun Wang (ORCID: https://orcid.org/0009-0004-4891-524X)
Institutions
- Hebei Medical University (CN)
- Children's National (US)
- Shandong University (CN)
- George Washington University (US)
- Hospital of Hebei Province (CN)
- Second Hospital of Shandong University (CN)
Publication Details
- Journal
- British Journal of Clinical Pharmacology
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1002/bcp.70050
- Citations
- 4
- Primary Topic
- Pneumonia and Respiratory Infections
- Type
- article
- Field-Weighted Citation Impact
- 5.42
Funders
- Shandong University
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
- National Key Research and Development Program of China