Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines
This study introduces a novel ML-based model integrating Th1/Th2 cytokines and clinical features to predict 30-day mortality in HM patients with BSIs, demonstrating strong performance and clinical applicability.
Authors
- Zuheng Wang
- Na Lu (ORCID: https://orcid.org/0000-0002-4981-5281)
- Nan Lin (ORCID: https://orcid.org/0000-0002-4635-3966)
- Zhenfang Liu (ORCID: https://orcid.org/0000-0002-6499-6607)
- Jirui Tang
- Yuexi Chen (ORCID: https://orcid.org/0000-0001-8720-5516)
- Yuling Xu (ORCID: https://orcid.org/0000-0003-1728-0339)
- Jing Li (ORCID: https://orcid.org/0000-0002-3837-3430)
- Xuemei Wang (ORCID: https://orcid.org/0009-0001-7403-2676)
- Qin Li (ORCID: https://orcid.org/0000-0003-3640-8625)
- Yuli Xie
- Min Xu (ORCID: https://orcid.org/0009-0003-5839-0259)
- Yan Huang (ORCID: https://orcid.org/0000-0003-4314-7267)
- Jiamin Luo (ORCID: https://orcid.org/0000-0003-2829-1914)
Institutions
- Guangxi Medical University (CN)
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1186/s12879-025-10808-7
- Citations
- 4
- Primary Topic
- Bacterial Identification and Susceptibility Testing
- Type
- article
- Field-Weighted Citation Impact
- 5.27
Funders
- National Natural Science Foundation of China