Using Machine Learning to Predict Linezolid-Associated Thrombocytopenia
We developed a machine learning model to predict linezolid-associated thrombocytopenia, with the random forest model achieving an AUC of 0.902. This model can help clinicians assess patient risk and optimize treatment plans. Future work should validate the model in multicenter studies and explore its integration into clinical decision support systems.
Authors
- Hong Zhou (ORCID: https://orcid.org/0000-0001-8183-2391)
- Xiujun Cai (ORCID: https://orcid.org/0000-0002-6457-0577)
- Zhuoling An (ORCID: https://orcid.org/0000-0002-7996-5002)
- Kexin Li (ORCID: https://orcid.org/0000-0002-2337-2427)
- Nian Liu (ORCID: https://orcid.org/0000-0001-5971-7995)
- Hao Wang (ORCID: https://orcid.org/0000-0003-3847-524X)
- Wei Rao (ORCID: https://orcid.org/0000-0002-6431-7272)
Institutions
- Beijing Chao-Yang Hospital, Capital Medical University (CN)
Publication Details
- Journal
- Infection and Drug Resistance
- Published
- 2025-05-01
- DOI
- https://doi.org/10.2147/idr.s479658
- Citations
- 1
- Primary Topic
- Platelet Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.77