Development and Validation of an Interpretable Machine Learning Model for Predicting Thrombocythemia Risk During Third Generation Cephalosporin Therapy
This interpretable ML framework enables precise thrombocythemia risk prediction during cephalosporin therapy, balancing algorithmic performance with clinical actionability.
Authors
- Maofeng Wang
- Kailei Du (ORCID: https://orcid.org/0000-0002-8942-9633)
- Ping Yu
Institutions
- Wenzhou Medical University (CN)
- Dongyang People's Hospital (CN)
Publication Details
- Journal
- Journal of Blood Medicine
- Published
- 2026-02-01
- DOI
- https://doi.org/10.2147/jbm.s567773
- Primary Topic
- Myeloproliferative Neoplasms: Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province