Machine learning approaches to personalize early prediction of asthma exacerbations.
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.
['Finkelstein|Joseph|J|', 'Jeong|In Cheol|IC|']
D000328:Adult D000465:Algorithms D001249:Asthma D001499:Bayes Theorem D003131:Combined Modality Therapy D019295:Computational Biology D020000:Decision Support Systems, Clinical D018450:Disease Progression D057286:Electronic Health Records D006801:Humans D020407:Internet D000069550:Machine Learning D008954:Models, Biological D008991:Monitoring, Physiologic D057285:Precision Medicine D011379:Prognosis D012372:ROC Curve D012648:Self Care D012720:Severity of Illness Index D017216:Telemedicine
machine learning prediction asthma exacerbation personalized medicine