Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator
We developed and validated a machine learning model capable of accurately predicting SAD with high clinical applicability. The integration of interpretable machine learning and an online calculator offers a practical tool to support early identification and timely management of SAD in critically ill patients.
Authors
- Ye Zhang
- Shuai Tong (ORCID: https://orcid.org/0000-0001-6570-5843)
- Xinda Yang (ORCID: https://orcid.org/0009-0003-0630-0506)
- Xu Wang (ORCID: https://orcid.org/0000-0002-1492-3477)
- Yun Chen (ORCID: https://orcid.org/0000-0002-9934-9979)
- Jin Bai (ORCID: https://orcid.org/0000-0002-2166-459X)
- Guangqi Wang (ORCID: https://orcid.org/0000-0002-6595-1857)
- Lang Gao (ORCID: https://orcid.org/0000-0002-6607-3240)
Institutions
- Qinghai University (CN)
- Fujian Medical University (CN)
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
- Hangzhou Medical College (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1371/journal.pone.0323831
- Citations
- 4
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 6.18