Machine learning in asthma research: moving toward a more integrated approach

Area of Science:

• Medical Informatics
• Computational Biology
• Respiratory Medicine

Background:

• Big data analytics are increasingly vital in medicine, reshaping research methodologies.
• Asthma research has evolved from hypothesis-driven to data-driven approaches to uncover complex patterns.
• Despite technological advances, translating big data insights into clinical asthma management remains limited.

Purpose of the Study:

• To conduct a bibliometric analysis of big data analytics in asthma research over the last 50 years.
• To evaluate the impact and utility of prevalent analytical methodologies in the medical field.
• To understand the progression and challenges of data-driven approaches in asthma research.

Main Methods:

• Bibliometric analysis of scientific literature.
• Review of big data analytical and computational methodologies in asthma research.
• Examination of research trends over a 50-year period.

Main Results:

• Identified a shift towards data-driven hypothesis generation in asthma studies.
• Highlighted the growing complexity and adoption of analytical methods in medicine.
• Observed a gap between research findings and clinical applicability in asthma.

Conclusions:

• No single data source or method can fully elucidate human health and disease complexity.
• Collaborative science and integrated, cross-disciplinary teams are essential for leveraging big data.
• Future efforts should focus on bridging the gap between big data insights and actionable clinical solutions in asthma.