Novel biomarker panel for the diagnosis and prognosis assessment of sepsis based on machine learning.
Biomark Med
; 16(15): 1129-1138, 2022 10.
Article
em En
| MEDLINE
| ID: mdl-36632836
ABSTRACT
Background:
The authors investigated a panel of novel biomarkers for diagnosis and prognosis assessment of sepsis using machine learning (ML) methods.Methods:
Hematological parameters, liver function indices and inflammatory marker levels of 332 subjects were retrospectively analyzed.Results:
The authors constructed sepsis diagnosis models and identified the random forest (RF) model to be the most optimal. Compared with PCT (procalcitonin) and CRP (C-reactive protein), the RF model identified sepsis patients at an earlier stage. The sepsis group had a mortality rate of 36.3%, and the RF model had greater predictive ability for the 30-day mortality risk of sepsis patients.Conclusion:
The RF model facilitated the identification of sepsis patients and showed greater accuracy in predicting the 30-day mortality risk of sepsis patients.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Sepse
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Biomark Med
Assunto da revista:
BIOQUIMICA
/
MEDICINA
Ano de publicação:
2022
Tipo de documento:
Article