Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.

Journal: Computer methods and programs in biomedicine

Published Date: Dec 9, 2019

Abstract

OBJECTIVES: Identifying acute exacerbations in chronic obstructive pulmonary disease (AECOPDs) is of utmost importance for reducing the associated mortality and financial burden. In this research, the authors aimed to develop identification models for AECOPDs and to compare the relative performance of different modeling paradigms to find the best model for this task.

Authors

Chenshuo Wang

Institute of Electronics, Chinese Academy of Sciences, Beijing, People's Republic of China. University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Xianxiang Chen
Lidong Du

Institute of Electronics, Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, China.
Qingyuan Zhan
Ting Yang

Northeastern University, Department of Chemistry, CHINA.
Zhen Fang

Keywords

Aged Algorithms Bayes Theorem Electronic Health Records Female Humans Logistic Models Machine Learning Male Middle Aged Predictive Value of Tests Pulmonary Disease, Chronic Obstructive ROC Curve Sensitivity and Specificity Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31841787)

Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.

Abstract

Authors

Keywords

External Resources

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