Predicting Delayed Extubation After General Anesthesia in Postanesthesia Care Unit Patients Using Machine Learning: Model Development Study
These machine learning models enable early identification of delayed extubation risk, supporting personalized clinical decisions and optimizing postanesthesia care unit resource allocation.
Authors
- Ning Liufu (ORCID: https://orcid.org/0000-0003-1600-0358)
- Shaoman Lin (ORCID: https://orcid.org/0000-0002-5932-5761)
- Tingkang Wang (ORCID: https://orcid.org/0009-0006-2515-5561)
- Jing-Sian Zheng (ORCID: https://orcid.org/0009-0009-3086-4719)
- Liman Wang (ORCID: https://orcid.org/0009-0009-7070-2335)
- Lin Chen (ORCID: https://orcid.org/0009-0006-1832-5550)
- Ziqi Lin (ORCID: https://orcid.org/0009-0000-2154-8014)
- Jianwei Luo (ORCID: https://orcid.org/0000-0002-6702-4483)
- Haixiang Ji (ORCID: https://orcid.org/0009-0000-0379-8052)
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-11-11
- DOI
- https://doi.org/10.2196/72602
- Primary Topic
- Anesthesia and Sedative Agents
- Type
- article
- Field-Weighted Citation Impact
- 0.00