Tree-based ensemble machine learning models in the prediction of acute respiratory distress syndrome following cardiac surgery: a multicenter cohort study
Our study identified 13 important features and provided multiple ML models to enhance the risk stratification for ARDS after cardiac surgery. Using these predictors and ML models might provide a basis for early diagnostic and preventive strategies in the perioperative management of ARDS patients.
Authors
- Hang Zhang (ORCID: https://orcid.org/0000-0002-6331-6649)
- Yongliang Fan (ORCID: https://orcid.org/0000-0002-6470-5945)
- Min Yu (ORCID: https://orcid.org/0000-0002-0042-2196)
- Xiaomiao Zhang (ORCID: https://orcid.org/0000-0002-3048-6748)
- Dewei Qian
- Weiran Huang (ORCID: https://orcid.org/0000-0003-1193-6157)
- Qingnan Zhao
- Tong‐Tong Gu
- Zheng Ruan
- Yuchen Wang (ORCID: https://orcid.org/0000-0002-6710-6710)
- Xin Chen (ORCID: https://orcid.org/0000-0002-7786-1701)
- Zhongxiang Yuan
- Yi Zhang (ORCID: https://orcid.org/0009-0001-8026-1635)
- Peize Meng (ORCID: https://orcid.org/0000-0002-1913-8945)
Institutions
- Shanghai Jiao Tong University (CN)
- XinHua Hospital (CN)
- Shanghai First People's Hospital (CN)
- Shanghai Sixth People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1186/s12967-024-05395-1
- Citations
- 12
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.64
Funders
- Nanjing Medical University
- Shanghai Hospital Development Center
- Fundamental Research Funds for the Central Universities