Machine learning-based model for acute asthma exacerbation detection using routine blood parameters
Background: Acute asthma exacerbations (AAEs) are a leading cause of asthma-related morbidity and mortality, especially in resource-limited settings where pulmonary function tests are unavailable or when patients are unable to cooperate with testing. This study aimed to develop and validate a diagno...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | World Allergy Organization Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1939455125000511 |
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| author | Youpeng Chen Junquan Sun Yabang Chen Enzhong Li Jiancai Lu Huanhua Tang Yifei Xie Jiana Zhang Lesi Peng Haojie Wu Zhangkai J. Cheng Baoqing Sun |
| author_facet | Youpeng Chen Junquan Sun Yabang Chen Enzhong Li Jiancai Lu Huanhua Tang Yifei Xie Jiana Zhang Lesi Peng Haojie Wu Zhangkai J. Cheng Baoqing Sun |
| author_sort | Youpeng Chen |
| collection | DOAJ |
| description | Background: Acute asthma exacerbations (AAEs) are a leading cause of asthma-related morbidity and mortality, especially in resource-limited settings where pulmonary function tests are unavailable or when patients are unable to cooperate with testing. This study aimed to develop and validate a diagnostic model for AAE using routine blood parameters through machine learning techniques. Methods: We developed a machine learning-based diagnostic model using routine blood test parameters. Data from 23,013 asthma patients treated at the First Affiliated Hospital of Guangzhou Medical University were analyzed. Significant variables were identified through logistic regression, and 12 machine learning algorithms were used to construct diagnostic models, which were evaluated using Receiver Operating Characteristic (ROC) analysis, calibration, and Decision Curve Analysis (DCA). Results: The Generalized Linear Model Boosting combined with Random Forest (glmBoost + RF) algorithm using 14 variables achieved comparable performance (Area Under the Curve [AUC] = 0.981) to the more complex Least Absolute Shrinkage and Selection Operator combined with Random Forest (Lasso + RF) algorithm using 25 variables (AUC = 0.985). Both models demonstrated excellent calibration and consistent performance across different demographic subgroups. DCA confirmed superior clinical utility compared to conventional strategies. Conclusions: This machine learning model provides an efficient and practical tool for detecting AAE using routine blood parameters, offering potential value in clinical practice, especially in resource-limited settings. Clinical trial number: Not applicable. |
| format | Article |
| id | doaj-art-50b1ff7f971541c28a1fa5ba1ea7c889 |
| institution | Kabale University |
| issn | 1939-4551 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | World Allergy Organization Journal |
| spelling | doaj-art-50b1ff7f971541c28a1fa5ba1ea7c8892025-08-20T03:32:20ZengElsevierWorld Allergy Organization Journal1939-45512025-07-0118710107410.1016/j.waojou.2025.101074Machine learning-based model for acute asthma exacerbation detection using routine blood parametersYoupeng Chen0Junquan Sun1Yabang Chen2Enzhong Li3Jiancai Lu4Huanhua Tang5Yifei Xie6Jiana Zhang7Lesi Peng8Haojie Wu9Zhangkai J. Cheng10Baoqing Sun11Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Guangzhou National Laboratory, Guangzhou, Guangdong Province, China; Guangdong Provincial Clinical Research Center for Laboratory Medicine, Guangzhou, Guangdong Province, ChinaFirst Affiliated Hospital of Guangzhou Medical University, Guangzhou, ChinaFirst Affiliated Hospital of Guangzhou Medical University, Guangzhou, ChinaFirst Affiliated Hospital of Guangzhou Medical University, Guangzhou, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Guangzhou National Laboratory, Guangzhou, Guangdong Province, China; Guangdong Provincial Clinical Research Center for Laboratory Medicine, Guangzhou, Guangdong Province, ChinaGuangzhou Medical University, Guangzhou, ChinaGuangzhou Medical University, Guangzhou, ChinaGuangzhou Medical University, Guangzhou, ChinaGuangzhou Medical University, Guangzhou, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Guangzhou National Laboratory, Guangzhou, Guangdong Province, China; Guangdong Provincial Clinical Research Center for Laboratory Medicine, Guangzhou, Guangdong Province, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Guangzhou National Laboratory, Guangzhou, Guangdong Province, China; Guangdong Provincial Clinical Research Center for Laboratory Medicine, Guangzhou, Guangdong Province, China; Corresponding author.Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Guangzhou National Laboratory, Guangzhou, Guangdong Province, China; Guangdong Provincial Clinical Research Center for Laboratory Medicine, Guangzhou, Guangdong Province, China; Corresponding author.Background: Acute asthma exacerbations (AAEs) are a leading cause of asthma-related morbidity and mortality, especially in resource-limited settings where pulmonary function tests are unavailable or when patients are unable to cooperate with testing. This study aimed to develop and validate a diagnostic model for AAE using routine blood parameters through machine learning techniques. Methods: We developed a machine learning-based diagnostic model using routine blood test parameters. Data from 23,013 asthma patients treated at the First Affiliated Hospital of Guangzhou Medical University were analyzed. Significant variables were identified through logistic regression, and 12 machine learning algorithms were used to construct diagnostic models, which were evaluated using Receiver Operating Characteristic (ROC) analysis, calibration, and Decision Curve Analysis (DCA). Results: The Generalized Linear Model Boosting combined with Random Forest (glmBoost + RF) algorithm using 14 variables achieved comparable performance (Area Under the Curve [AUC] = 0.981) to the more complex Least Absolute Shrinkage and Selection Operator combined with Random Forest (Lasso + RF) algorithm using 25 variables (AUC = 0.985). Both models demonstrated excellent calibration and consistent performance across different demographic subgroups. DCA confirmed superior clinical utility compared to conventional strategies. Conclusions: This machine learning model provides an efficient and practical tool for detecting AAE using routine blood parameters, offering potential value in clinical practice, especially in resource-limited settings. Clinical trial number: Not applicable.http://www.sciencedirect.com/science/article/pii/S1939455125000511AsthmaMachine learningBlood chemical analysisDiagnosis |
| spellingShingle | Youpeng Chen Junquan Sun Yabang Chen Enzhong Li Jiancai Lu Huanhua Tang Yifei Xie Jiana Zhang Lesi Peng Haojie Wu Zhangkai J. Cheng Baoqing Sun Machine learning-based model for acute asthma exacerbation detection using routine blood parameters World Allergy Organization Journal Asthma Machine learning Blood chemical analysis Diagnosis |
| title | Machine learning-based model for acute asthma exacerbation detection using routine blood parameters |
| title_full | Machine learning-based model for acute asthma exacerbation detection using routine blood parameters |
| title_fullStr | Machine learning-based model for acute asthma exacerbation detection using routine blood parameters |
| title_full_unstemmed | Machine learning-based model for acute asthma exacerbation detection using routine blood parameters |
| title_short | Machine learning-based model for acute asthma exacerbation detection using routine blood parameters |
| title_sort | machine learning based model for acute asthma exacerbation detection using routine blood parameters |
| topic | Asthma Machine learning Blood chemical analysis Diagnosis |
| url | http://www.sciencedirect.com/science/article/pii/S1939455125000511 |
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