Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

哮喘是儿童和成人中常见的慢性呼吸道疾病，影响全球超过 2 亿人，每年导致约 45 万人死亡。机器学习越来越多地应用于医疗保健领域，以协助医疗从业者做出决策。在哮喘管理中，机器学习擅长执行明确的任务，例如诊断、预测、药物治疗和管理。然而，如何应用机器学习来预测哮喘恶化仍存在不确定性。本研究旨在系统回顾机器学习技术在预测哮喘发作风险方面的最新应用，以协助哮喘控制和管理。最初从五个数据库中确定了总共 860 项研究。经过筛选和全文评审，最终选出 20 项研究纳入本次综述。该综述考虑了 2010 年 1 月至 2023 年 2 月发表的最新研究。这 20 项研究使用机器学习技术，通过使用临床、医学、生物和社会人口数据源以及环境数据等各种数据源来支持未来的哮喘风险预测。和气象数据。虽然一些研究将预测视为一个类别，但其他研究预测了病情恶化的可能性。只有一组研究应用了预测窗口。该论文提出了一个概念模型来总结如何利用机器学习和可用数据源来生成早期检测哮喘发作的有效模型。该审查还生成了其他研究人员可以在类似工作中使用的数据源列表。此外，我们还提出了进一步研究的机会以及先前研究的局限性。