[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126062-en":3,"doc-seo-126062-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126062,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting asthma attacks in New Zealand using machine learning - Conference Abstract","Identifying factors that increase asthma-attack risk enables timely patient management, and machine learning increasingly supports risk prediction. This study aims to determine risk factors for asthma attacks in New Zealand and compare Extreme Gradient Boosting and Random Forest against Logistic Regression. Using national health datasets (355,113 patients aged ≥6) from 2008–2016, the outcome was an asthma attack occurrence within 3 months.","TOP MENU  \nERS website  \n| Respiratory Channel |\n| --- |\n| ERS Publications |\n\nMy Account  \n􀃍  \n0 items  \nSearch within ERS publications   \n Conference Abstract  􀇏 Free  \nPredicting asthma attacks in New Zealand using machine learning  \nWidana Kankanamge Darsha Jayamini M. Asif Naeem  Show More 􀀲  \nFarhaan Mirza  \nEuropean Respiratory Journal 2024 64(suppl 68): PA783; DOI:  \n[https://doi.org/10.1183/13993003.congress-2024.PA783](https://doi.org/10.1183/13993003.congress-2024.PA783)  \n􀂀 Permissions  \n􀀯 Add to Favourites  \n􀃟 Labels  \n􀂕 Cite  \n􀃊 Share  \n􀀢 Alerts  \nThis article appears in:  \nEuropean Respiratory Journal  \nVol 64 Issue suppl 68  \n􀂑 Focus 􀀳 Previous Next 􀀴  \n\n| Article | Figures & Tables | Info & Metrics |\n| --- | --- | --- |\n\nAbstract  \nIntroduction: Identifying the factors that increase the risk of asthma attacks is key for timely patient management. Machine learning ( ML) techniques have been increasingly used for risk prediction.  \nAims: To identify risk factors for asthma attacks in New Zealand ( NZ) and evaluate the performa ML algorithms in predicting the risk of asthma attacks.  \nMethods: NZ National health datasets from 355, 113 patients ≥6 years old with asthma were analysed between 2008 and 2016. The modelled outcome was an occurrence of an asthma attack in 3 months. Two ML models-Extreme Gradient Boosting (XGB) and Random Forest ( RF) -and one statistical model -a Logistic Regression ( LR) were developed. Feature selection, data pre-processing, imbalance handling and hyperparameter tuning were conducted.  \nResults: Prior history of asthma attacks, length of exposure to the winter season, number of inhaled corticosteroids (ICS) and short acting beta-agonist (SABA) inhalers were important risk predictors (Fig 1) . Overall, XGB with random under-sampling performed marginally better (Area Under the Receiver Operating Curve=0 .76 ( F 1 score=0 .27, PPV=0 .173, NPV=0 .962, Sensitivity =0 .622, Specificity =0 .763) .  \nConclusion: ML models performed marginally better than LR in asthma attack prediction. Future research to explore other ML and data imbalance handling techniques is needed to enhance risk prediction.  \n􀂑 Focus 􀀳 Previous Next 􀀴  \nWe recommend  \nLATE-BREAKING ABSTRACT: Predicting asthma at age 8; the application of machine learning  \nSilvia Colicino , European Respiratory Journal , 2016  \nDifferentiating COPD and Asthma using Quantitative CT Imaging and Machine Learning  \nAmir Moslemi , European Respiratory Journal , 2022  \nMachine Learning for Predicting Health Care Utilization in COPD using Quantitative CT Imaging  \nAmir Moslemi , European Respiratory Journal  \nUse of Machine learning to predict asthma exacerbations  \nChrister Janson , European Respiratory Journal  \nAn End-to-End Machine Learning Framework for Predicting Common Geriatric Diseases   \nJian Guo , Journal of Beijing Institute of Technology, 2023  \nDiscrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm  \nGeoscience Frontiers , 2023  \nUltrasonic prediction of crack density using machine learning:  \nA numerical investigation   \nSadegh Karimpouli , Geoscience Frontiers , 2022 Self-directed machine learning   \nWenwu Zhu , AI Open , 2022  \nAutomated identification of asthma patients within an electronical medical record database using machine learning Marjolein Engelkes , European Respiratory Journal , 2012  \nAdvancing agriculture with machine learning: a new frontier in weed management   \nFrontiers of Agricultural Science and Engineering , 2024  \nPowered by    \nUse of Machine learning to predict asthma exa...  \nPredicting real-world response to mepolizumab...  \nM  \n Related Articles   \nPredicting paediatric asthma exacerbations wi...  \nSh  \now ore 􀀲  \nSevere Asthma  \nPaediatric Asthma  \nM  \n Related Books   \nNew Developments in Mechanical Ventilation  \nSh  \now ore 􀀲  \nRelated Book Chapters  \nAsthma at school age and in adolescence  \nSupported self-management in asthma  \nEpidemiol","cbCaiaWJDl5yMmm5","https://ap.wps.com/l/cbCaiaWJDl5yMmm5","pdf",631065,6,1,5,"English","en",105,"# Abstract\n## Introduction\n## Aims\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To identify risk factors for asthma attacks in New Zealand and evaluate machine learning algorithm performance for predicting these risks.\"},{\"question\":\"How was the prediction target defined?\",\"answer\":\"An asthma attack occurrence within 3 months was used as the modeled outcome.\"},{\"question\":\"Which risk predictors were found to be important?\",\"answer\":\"Prior history of asthma attacks, length of winter-season exposure, and the number of inhaled corticosteroids (ICS) and short-acting beta-agonist (SABA) inhalers.\"}]","Predicting asthma attacks in New Zealand using machine learning - 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