Machine learning approaches to personalize early prediction of asthma exacerbations

• Published in: Annals of the New York Academy of Sciences Vol. 1387; no. 1; pp. 153 - 165
• Main Authors: Finkelstein, Joseph, Jeong, In cheol
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01-01-2017
• Subjects: Adult; Algorithms; Asthma; Asthma - diagnosis; Asthma - physiopathology; Asthma - therapy; asthma exacerbation; Bayes Theorem; Chronic illnesses; Combined Modality Therapy; Computational Biology; Decision Support Systems, Clinical; Disease Progression; Electronic Health Records; Humans; Internet; Machine Learning; Models, Biological; Monitoring, Physiologic; Patients; personalized medicine; Precision Medicine; prediction; Prognosis; ROC Curve; Self Care; Severity of Illness Index; Telemedicine - methods; prediction; asthma exacerbation; machine learning; personalized medicine
• DOI: 10.1111/nyas.13218
• EISSN: 1749-6632
• ISSN: 0077-8923

Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self‐monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training datasets, predictive feature selection, and evaluation of resulting classifiers. Using a 7‐day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.