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

• Published in: Journal of medical systems Vol. 48; no. 1; p. 49
• Main Authors: Darsha Jayamini, Widana Kankanamge, Mirza, Farhaan, Asif Naeem, M., Chan, Amy Hai Yan
• Format: Journal Article
• Language: English
• Published: New York Springer US 13.05.2024; Springer Nature B.V
• Subjects: Asthma; Asthma - diagnosis; Data sources; Decision making; Disease Progression; Health Informatics; Health risks; Health Sciences; Humans; Learning algorithms; Machine Learning; Medicine; Medicine & Public Health; Meteorological data; Predictions; Respiratory diseases; Review; Reviews; Risk; Risk Assessment - methods; Statistics for Life Sciences; Systematic review; Risk of attack; Prediction; Machine learning; Asthma
• ISSN: 1573-689X; 0148-5598; 1573-689X
• DOI: 10.1007/s10916-024-02061-3

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.