Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms
The RF model demonstrates high and consistent effectiveness in predicting pulmonary embolism risk in tuberculosis patients.
Authors
- Zhi Geng (ORCID: https://orcid.org/0000-0003-1798-4475)
- Haobo Kong
- Jingjing Pan (ORCID: https://orcid.org/0000-0003-2887-9881)
- Min Liang (ORCID: https://orcid.org/0000-0003-3603-861X)
- Yong Li (ORCID: https://orcid.org/0000-0002-0227-5388)
- Ya Shen (ORCID: https://orcid.org/0000-0002-4948-9666)
- Yanbei Zhang (ORCID: https://orcid.org/0000-0002-1035-3681)
Institutions
- Anhui Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Anhui Mental Health Center (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1186/s40001-024-02218-3
- Citations
- 2
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.10
Funders
- Natural Science Foundation of Anhui Province