Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study
The optimal ML model, which was developed by clinical characteristics, allows for the satisfactory prediction of AS in patients after LTx.
Authors
- Dong Tian (ORCID: https://orcid.org/0000-0001-8510-8309)
- Ming-Zhao Liu
- Hang Yang (ORCID: https://orcid.org/0000-0002-7349-3366)
- Jin Zhao (ORCID: https://orcid.org/0000-0001-5428-2187)
- Lingzhi Shi
- Yu-Jie Zuo
- Hao‐Ji Yan (ORCID: https://orcid.org/0000-0003-4420-4657)
- Jingyu Chen (ORCID: https://orcid.org/0000-0002-2127-1788)
- Heng Huang (ORCID: https://orcid.org/0009-0006-2868-9323)
Institutions
- Sichuan University (CN)
- Juntendo University (JP)
- West China Hospital of Sichuan University (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Wuxi People's Hospital (CN)
- Nanjing Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1186/s12911-024-02635-8
- Citations
- 3
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- National Natural Science Foundation of China