Machine learning models can predict cancer-associated disseminated intravascular coagulation in critically ill colorectal cancer patients
The results indicate that ML models, particularly the XGB model, can serve as effective tools for predicting overt DIC in critically ill CRC patients. This offers a promising approach to improving clinical decision-making in this high-risk group.
Authors
- Jun Mao (ORCID: https://orcid.org/0000-0001-5275-8954)
- Min Gao (ORCID: https://orcid.org/0000-0003-0127-7477)
- Jingwen Xie (ORCID: https://orcid.org/0000-0002-5594-8283)
- Qin Li (ORCID: https://orcid.org/0000-0002-1361-527X)
- Xiaoyan Li (ORCID: https://orcid.org/0000-0003-3145-0517)
- Zhikun Liang (ORCID: https://orcid.org/0000-0002-4417-6011)
Institutions
- Sun Yat-sen University (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
Publication Details
- Journal
- Frontiers in Pharmacology
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3389/fphar.2024.1478342
- Citations
- 4
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.55