败血症
机器学习
重症监护医学
数据科学
鉴定(生物学)
精密医学
医学
人工智能
计算机科学
范围(计算机科学)
个性化
临床试验
生物信息学
病理
免疫学
生物
万维网
程序设计语言
植物
作者
Matthieu Komorowski,Ashleigh Green,Kate Tatham,Christopher Seymour,David Antcliffe
出处
期刊:EBioMedicine
[Elsevier]
日期:2022-12-01
卷期号:86: 104394-104394
被引量:38
标识
DOI:10.1016/j.ebiom.2022.104394
摘要
Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis.Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes.It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management.Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning.This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.
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