Machine learning–based prediction model for hypofibrinogenemia after tigecycline therapy
The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy.
Authors
- Jianping Zhu (ORCID: https://orcid.org/0000-0002-4094-5450)
- Rui Zhao (ORCID: https://orcid.org/0000-0002-9577-7714)
- Zhenwei Yu (ORCID: https://orcid.org/0000-0002-3776-2290)
- Liucheng Li
- Yan Guan
- Jiayue Wei
Institutions
- Sir Run Run Shaw Hospital (CN)
- Zhejiang Cancer Hospital (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-10-04
- DOI
- https://doi.org/10.1186/s12911-024-02694-x
- Citations
- 3
- Primary Topic
- Blood properties and coagulation
- Type
- article
- Field-Weighted Citation Impact
- 1.26