Research on algorithms for identifying the severity of acute respiratory distress syndrome patients based on noninvasive parameters

Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operat...

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Bibliographic Details
Published in:Sheng wu yi xue gong cheng xue za zhi 2019-06, Vol.36 (3), p.435
Main Authors: Yang, Pengcheng, Chen, Feng, Zhang, Guang, Yu, Ming, Lu, Meng, Wang, Chunchen, Wang, Chunfei, Wu, Taihu
Format: Article
Language:Chinese
Subjects:
Accuracy
Algorithms
Area Under Curve
Bagging
Blood
Blood Gas Analysis
Classification
Diagnosis
Gas analysis
Health risks
Humans
Learning algorithms
Machine Learning
Medical personnel
Monitoring, Physiologic - methods
Neural networks
Oxygenation
Parameter identification
Physiology
Regression analysis
Respiratory distress syndrome
Respiratory Distress Syndrome - diagnosis
ROC Curve
Sensitivity and Specificity
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Summary:Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can't continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected
ISSN:1001-5515
DOI:10.7507/1001-5515.201801081