In Biomarkers in medicine
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.
Wu Juehui, Liang Jianbo, An Shu, Zhang Jingcong, Xue Yimin, Zeng Yanlin, Li Laisheng, Luo Jinmei
2023-Jan-12
biomarkers, diagnosis, machine learning, prognosis, sepsis
