Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

Area of Science:

• Healthcare technology
• Computational medicine
• Respiratory medicine

Background:

• Asthma management remains challenging despite advances, with many patients experiencing acute exacerbations.
• Disease activity is influenced by numerous factors including medical history, environment, and patient habits.
• Current asthma treatment regimens do not fully prevent exacerbations.

Purpose of the Study:

• To review the current evidence of artificial intelligence (AI) and machine learning (ML) in asthma management.
• To explore the potential of AI and ML in predicting asthma exacerbations.
• To identify future applications and challenges of AI/ML in clinical asthma care.

Main Methods:

• Literature review of existing scientific evidence on AI and ML in asthma.
• Analysis of studies demonstrating AI/ML's predictive capabilities for asthma exacerbations.
• Synthesis of factors influencing asthma exacerbations and their integration into AI/ML models.

Main Results:

• AI and ML show promise in accurately predicting asthma exacerbations.
• These technologies can integrate a wide range of patient-specific data for comprehensive analysis.
• Existing research highlights the potential but also the limitations of current AI/ML applications.

Conclusions:

• AI and ML offer a promising approach to revolutionize personalized asthma management.
• Clinical application of AI/ML in asthma care is still in its early stages.
• Further research is essential to overcome implementation barriers and optimize AI/ML for asthma exacerbation prediction.