Machine learning approaches for asthma exacerbation predictions: a systematic review

Abstract

Abstract Asthma exacerbations are critical events that can lead to severe health complications, hospitalizations, and increased healthcare costs. Accurate prediction of these exacerbations is essential for timely intervention and improved patient outcomes. Traditional statistical models face challenges in handling the high-dimensional nature of clinical and environmental data. In this context, machine learning (ML) techniques offer promising alternatives for predicting asthma exacerbations by leveraging diverse data sources, that are often high-dimensional, including electronic health records, environmental factors, and patient-reported outcomes. This systematic review evaluates the application of ML-style models, including the use of logistic regression, decision trees, gradient boosting machines, support vector machines, and deep learning approaches such as long short-term memory networks, for the prediction of asthma exacerbations. Our findings indicate that from model performance assessment point of view ensemble learning methods, particularly random forests and boosting, consistently achieve higher accuracy than the traditional statistical models. Moreover, neural networks and deep learning models show potential in capturing complex temporal dependencies associated with exacerbation risk. From a clinical perspective, the literature shows that traditional models such as logistic regression remain highly valued for their interpretability and alignment with clinical reasoning, allowing clinicians to identify actionable and biologically plausible risk factors for exacerbations. At the same time, more advanced ML approaches add clinical value by capturing temporal dynamics, environmental influences, and patient subgroups, but their adoption in practice depends critically on transparency and clear explanation of the drivers of risk. However, challenges remain, including model interpretability, generalizability across different populations, and integration into clinical practice. Future research should focus on enhancing explainability, improving data harmonization, and optimizing hybrid ML frameworks to develop robust predictive models for asthma management. This review highlights the need for interdisciplinary collaboration to translate ML advancements into clinically relevant applications, ultimately improving asthma care and reducing exacerbation-related morbidity.

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