A scoping review of asthma and machine learning

Abstract

Objective

The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature.

Data Sources

We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020.

Study Selection

DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented.

Results

A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status).

Conclusions

The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence).

Supplemental data for this article is available online at http://dx.doi.org/ .

Keywords:

• Asthma
• epidemiology
• machine learning

Acknowledgements

Health Sciences Program, College of Medicine, University of Saskatchewan, Canadian Center for Health and Safety in Agriculture (CCHSA).

Declaration of interest

The authors report no conflicts of interest.

Financial

This research has been supported by personal scholarships from the University of Saskatchewan University Graduate Scholarship (UGS), Canadian Centre for Health and Safety in Agriculture Founding Chairs Fellowships, and Entrance Scholarship from the University of Saskatchewan.

Funding

The author(s) reported there is no funding associated with the work featured in this article.