Comparative performance of machine learning and conventional scoring systems for neuroprognostication in out-of-hospital cardiac arrest survivors
PROLOGUE was the best performing conventional score. CAHP, although slightly less accurate, used simpler variables. The ML model outperformed the conventional scores, but its advantage over PROLOGUE was not substantial. Clinicians should select predictive tools based on the clinical context.
Authors
- Chun‐Hsiang Huang (ORCID: https://orcid.org/0000-0001-8281-3192)
- Chih‐Wei Sung (ORCID: https://orcid.org/0000-0003-3312-2752)
- Cheng‐Yi Fan (ORCID: https://orcid.org/0000-0002-1585-0038)
- Wei‐Tien Chang (ORCID: https://orcid.org/0000-0002-1880-7932)
- Sih‐Shiang Huang (ORCID: https://orcid.org/0000-0002-9388-4786)
- Chun‐Ju Lien
- Ching‐Yu Chen (ORCID: https://orcid.org/0000-0002-4095-4197)
- Yi‐Ju Ho (ORCID: https://orcid.org/0000-0002-8694-2642)
- Chi‐Hsin Chen (ORCID: https://orcid.org/0000-0003-1826-6598)
- Chien‐Tai Huang (ORCID: https://orcid.org/0009-0003-7999-2689)
- Yi‐Chien Kuo
- Chien-Hua Huang
- Edward Pei-Chuan Huang
Institutions
- National Taiwan University Hospital (TW)
Publication Details
- Journal
- Journal of the Formosan Medical Association
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1016/j.jfma.2025.09.003
- Citations
- 1
- Primary Topic
- Cardiac Arrest and Resuscitation
- Type
- article
- Field-Weighted Citation Impact
- 2.25