Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.

IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Systems Pub Date : 2024-05-13 DOI:10.1007/s10916-024-02061-3
Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan
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Abstract

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.

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研究预测哮喘恶化风险的机器学习技术:系统回顾
哮喘是儿童和成人中常见的慢性呼吸道疾病,影响着全球 2 亿多人,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,以协助医疗从业人员做出决策。在哮喘管理中,机器学习在执行诊断、预测、用药和管理等明确任务方面表现出色。然而,如何将机器学习应用于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。初步从五个数据库中确定了 860 项研究。经过筛选和全文审阅后,20 项研究被选入本综述。综述考虑了 2010 年 1 月至 2023 年 2 月期间发表的最新研究。这 20 项研究利用机器学习技术,通过使用各种数据源,如临床、医疗、生物和社会人口数据源,以及环境和气象数据,支持未来哮喘风险预测。一些研究将预测作为一个类别,而其他研究则预测病情恶化的概率。只有一组研究使用了预测窗口。本文提出了一个概念模型,总结了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。该综述还生成了一份数据源清单,其他研究人员可在类似工作中使用这些数据源。此外,我们还提出了进一步研究的机会以及前述研究的局限性。
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来源期刊
Journal of Medical Systems
Journal of Medical Systems 医学-卫生保健
CiteScore
11.60
自引率
1.90%
发文量
83
审稿时长
4.8 months
期刊介绍: Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician''s office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.
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