Machine Learning‐Based Identification of Preoperative Psychological Distress and Its Association With Adverse Surgery‐Related Outcomes: Evidence From the China Surgery and Anesthesia Cohort (CSAC)
Our findings demonstrate the effectiveness of machine learning in accurately identifying patients with preoperative psychological distress who may require clinical attention, highlighting the potential of these techniques to guide targeted preoperative interventions and ultimately improve surgical outcomes.
Authors
- Qian Li (ORCID: https://orcid.org/0009-0000-4732-6233)
- Yu Zeng (ORCID: https://orcid.org/0000-0002-0593-7551)
- Huan Song (ORCID: https://orcid.org/0000-0003-3845-8079)
- Lei Yang (ORCID: https://orcid.org/0000-0003-0232-7915)
- Yao Yang (ORCID: https://orcid.org/0000-0003-1781-5452)
- Wenwen Chen (ORCID: https://orcid.org/0000-0002-2586-9234)
- Can Hou (ORCID: https://orcid.org/0000-0002-9872-786X)
Institutions
- University of Iceland (IS)
- Sichuan University (CN)
- West China Medical Center of Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- Depression and Anxiety
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1155/da/3990416
- Citations
- 1
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Sichuan University
- West China Hospital, Sichuan University