Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning–Based Predictive Model
We developed and validated a generalizable RF model to predict postoperative SIRS in older adult patients, enabling clinicians to screen susceptible and high-risk patients and implement early individualized interventions. An online risk calculator to make the RF model accessible to anesthesiologists and peers around the world was developed.
Authors
- Tongsen Luo (ORCID: https://orcid.org/0000-0003-1284-0712)
- Chaojin Chen (ORCID: https://orcid.org/0000-0001-5101-4101)
- Yu Fu (ORCID: https://orcid.org/0009-0003-4499-3323)
- Yuenong Zhang (ORCID: https://orcid.org/0000-0002-2318-3204)
- Yaxin Lu (ORCID: https://orcid.org/0000-0001-6278-4347)
- Yihan Zhang (ORCID: https://orcid.org/0000-0002-3546-9752)
- Xiaoyue Li (ORCID: https://orcid.org/0000-0001-9149-7152)
- Shaoli Zhou (ORCID: https://orcid.org/0000-0002-6604-0166)
- Zifeng Liu (ORCID: https://orcid.org/0000-0002-1392-8698)
- Haiyan Mai (ORCID: https://orcid.org/0009-0007-7022-173X)
Institutions
- Sun Yat-sen University (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-11-04
- DOI
- https://doi.org/10.2196/57486
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00