A scoping review of asthma and machine learning.

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/ .

Are Dietary Factors Associated with Lung Function in Canadian Adults?

Purpose: The purpose of this study was to explore the impact of dietary factors and biomarkers on lung function among Canadian adults (18-79 years). Methods: Our data source was the Canadian Health Measures Survey Cycle-3, which included 3397 adults. The household and clinic questionnaires and physical measures were used to assess individual dietary factors, modified Mediterranean Diet Scores, and biomarkers. Results: The overall mean percent predicted values for FVC and FEV1 were 97% and 95%, respectively. While somewhat inconsistent between outcomes, higher lung function was associated with bean, grain, milk, fruit, and vegetable consumption, whereas lower lung function was associated with egg and potato consumption. Among the biomarkers, vitamin D, chloride, total serum protein, and red blood cell folate were associated with higher lung function, whereas C-reactive protein and vitamin B12 was associated with lower lung function. Conclusion: Our study provides support for an association between some dietary factors and lung function, though not entirely consistent between a specific dietary factor and the outcomes studied (FVC, FEV1, FVC/FEV1, and FEF25%-75%). The associations between a specific biomarker and lung function were more consistent (i.e., observed with a larger number of lung function outcomes) than were the dietary factors.