Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis

Background and objectives Sepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine lea...

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Veröffentlicht in:	BMC medical informatics and decision making Jg. 23; H. 1; S. 283 - 12
Hauptverfasser:	Zhang, Yan, Xu, Weiwei, Yang, Ping, Zhang, An
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 11.12.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Analysis Bias Care and treatment Cohort analysis Complications and side effects Critical care Databases, Factual Death Emergency medical care Emergency medical services Emergency Service, Hospital Health aspects Health Informatics Health risk assessment Hospitals Humans Infection Information Systems and Communication Service Intensive care units Lactates Lactic Acid Learning algorithms Machine Learning Management of Computing and Information Systems Medical research Medicine Medicine & Public Health Medicine, Experimental Meta-analysis Methods Model accuracy Mortality Mortality risk Patients Physiology Prediction models Predictions Risk assessment Sensitivity Sepsis Sepsis - diagnosis Subgroups Subject heading schemes Systematic review China Systematic review Sepsis Mortality Machine learning Meta-analysis
ISSN:	1472-6947, 1472-6947
Online-Zugang:	Volltext
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