Asthma exacerbation events remain difficult to avert and approaches for personalized medicine are lacking.

Each patient’s asthma journey and disease severity are influenced by a range of factors including medical history, biomarker phenotype, pulmonary function, level of healthcare system support, compliance to prescribed therapy, comorbidities, personal habits, and environmental conditions.

Machine learning (ML) uses mathematical and statistical methods to detect patterns across large datasets including electronic health records (EHR).

ML has the potential to augment clinical decision-making and provide appropriate treatment to improve both asthma prognosis and the overall quality of life.

In this review, we summarize recent studies that have demonstrated the ability to predict asthma exacerbations using different algorithms and propose a few next steps for this field.