Progression in the application of machine learning in acute respiratory distress syndrome.
10.3760/cma.j.cn121430-20221027-00944
- Author:
Weijun ZHANG
1
;
Jianxiao CHEN
;
Yuan GAO
Author Information
1. Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China. Corresponding author: Gao Yuan, Email: rj_gaoyuan@163.com.
- Publication Type:Journal Article
- MeSH:
Humans;
Hypoxia/complications*;
Respiratory Distress Syndrome/etiology*;
Prognosis;
Machine Learning
- From:
Chinese Critical Care Medicine
2023;35(6):662-664
- CountryChina
- Language:Chinese
-
Abstract:
Acute respiratory distress syndrome (ARDS) is a clinical syndrome defined by acute onset of hypoxemia and bilateral pulmonary opacities not fully explained by cardiac failure or volume overload. At present, there is no specific drug treatment for ARDS, and the mortality rate is high. The reason may be that ARDS has rapid onset, rapid progression, complex etiology, and great heterogeneity of clinical manifestations and treatment. Compared with traditional data analysis, machine learning algorithms can automatically analyze and obtain rules from complex data and interpret them to assist clinical decision making. This review aims to provide a brief overview of the machine learning progression in ARDS clinical phenotype, onset prediction, prognosis stratification, and interpretable machine learning in recent years, in order to provide reference for clinical.
