Machine learning prediction model for delirium after heart valve replacement with cardiopulmonary bypass: A large-scale cohort study
Machine learning models, particularly Random Forest, provide a robust and generalizable tool for the early prediction of POD after HVR. These models outperform traditional statistical approaches and offer interpretable insights, highlighting PAT as a critical, modifiable target for perioperative management to mitigate POD risk.
Authors
- Rui An (ORCID: https://orcid.org/0009-0008-5233-3954)
- Ruiping Bai (ORCID: https://orcid.org/0000-0002-4190-3105)
- Kunyu Han
- Jin Li (ORCID: https://orcid.org/0000-0002-6131-456X)
- Xin Shen (ORCID: https://orcid.org/0000-0002-7735-7329)
- Mengwen Xue (ORCID: https://orcid.org/0000-0002-7786-8081)
- Li Zhang
- Di Peng
- Linjuan Zhang
- Bingxin Zhou
Institutions
- Xi'an Honghui Hospital (CN)
- Nanjing Drum Tower Hospital (CN)
- Shandong Provincial Hospital (CN)
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
- Lanzhou University Second Hospital (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Asian Journal of Psychiatry
- Published
- 2026-03-07
- DOI
- https://doi.org/10.1016/j.ajp.2026.104930
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00