Application progress of machine learning algorithms in the diagnosis,treatment and prognosis evaluation of sepsis-induced coagulopathy
Tan Ruimin
Ge Chen
Guo He
Yan Yating
Han Xumin
Zhao Tianyu
Wen Kexin
Du Quansheng
Abstract：Sepsis can cause multiple organ failure by excessive interaction between immunity and coagulation,Sepsis-induced coagulopathy(SIC)is one of the important factors leading to poor prognosis.With the continuous development of artificial intelligence,the application of machine learning has become more and more common in the medical field.Machine learning refers to the automatic learning of underlying laws from training set data,and these laws are used for classification,prediction,and decision-making of new data.The application of machine learning in SIC can help physicians detect the disease earlier and take effective interventions to reduce the disease severity and improve the patient's prognosis.This review summarizes the pathogenesis of SIC,the research progress of machine learning algorithm in the diagnosis,treatment and prognosis evaluation of SIC,and discusses its advantages and disadvantages as well as future research directions and improvement measures in order to provide directions for future research and clinical treatment.
Keywords：Machine learningSepsis-induced coagulopathyDiagnosisTreatmentPrognosis evaluation
Publication Date：2025-09-10
Online Publishing Date：2025-10-17(First online date of this platform, not the publication date of the document)
Pages：6( 803-808 )