Novel biomarker panel for the diagnosis and prognosis assessment of sepsis based on machine learning.
Juehui WuJianbo LiangShu AnJingcong ZhangYimin XueYanlin ZengLaisheng LiJinmei LuoPublished in: Biomarkers in medicine (2023)
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.
Keyphrases
- acute kidney injury
- intensive care unit
- septic shock
- end stage renal disease
- machine learning
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- cardiovascular events
- cardiovascular disease
- oxidative stress
- patient reported outcomes
- climate change
- big data
- high resolution
- neural network