A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.

Tytuł:: A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.
Autorzy:: Peng J; Sun Yat-sen University, School of Data and Computer Science, Guangzhou, 510006, China.
Chen C; Sun Yat-sen University, School of Data and Computer Science, Guangzhou, 510006, China.
Zhou M; Sun Yat-sen University, The Third Affiliated Hospital, Guangzhou, 510640, China.
Xie X; Sun Yat-sen University, School of Data and Computer Science, Guangzhou, 510006, China.
Zhou Y; Sun Yat-sen University, The Third Affiliated Hospital, Guangzhou, 510640, China. .
Luo CH; Sun Yat-sen University, School of Data and Computer Science, Guangzhou, 510006, China. .
Źródło:: Scientific reports [Sci Rep] 2020 Feb 20; Vol. 10 (1), pp. 3118. Date of Electronic Publication: 2020 Feb 20.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:: Decision Support Systems, Clinical*
Decision Trees*
Machine Learning*
Pulmonary Disease, Chronic Obstructive/*physiopathology
Adult ; Aged ; Aged, 80 and over ; Algorithms ; Comorbidity ; Disease Progression ; False Positive Reactions ; Female ; Hospitalization ; Humans ; Inflammation ; Male ; Middle Aged ; Models, Statistical ; Prognosis ; ROC Curve ; Reproducibility of Results ; Risk
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Entry Date(s):: Date Created: 20200222 Date Completed: 20201230 Latest Revision: 20210806
Update Code:: 20240105
PubMed Central ID:: PMC7033165
DOI:: 10.1038/s41598-020-60042-1
PMID:: 32080330
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Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and prediction methods for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients during hospitalization. To enhance the monitoring and treatment of AECOPD patients, we develop a novel C5.0 decision tree classifier to predict the prognosis of AECOPD hospitalized patients with objective clinical indicators. The medical records of 410 hospitalized AECOPD patients are collected and 28 features including vital signs, medical history, comorbidities and various inflammatory indicators are selected. The overall accuracy of the proposed C5.0 decision tree classifier is 80.3% (65 out of 81 participants) with 95% Confidence Interval (CI):(0.6991, 0.8827) and Kappa 0.6054. In addition, the performance of the model constructed by C5.0 exceeds the C4.5, classification and regression tree (CART) model and the iterative dichotomiser 3 (ID3) model. The C5.0 decision tree classifier helps respiratory physicians to assess the severity of the patient early, thereby guiding the treatment strategy and improving the prognosis of patients.

Erratum in: Sci Rep. 2021 Mar 2;11(1):5324. (PMID: 33649404)

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