title: Predicting asthma control deterioration in children.
abstract: BACKGROUND
  Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence.
  
  
  METHODS
  We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead.
  
  
  RESULTS
  Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic.
  
  
  CONCLUSIONS
  Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT, 84108, USA. gang.luo@utah.edu.
authors.forename: Gang
authors.initials: G
authors.lastname: Luo
authors.affiliation: Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT, 84113, USA.
authors.forename: Bryan L
authors.initials: BL
authors.lastname: Stone
authors.affiliation: Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT, 84113, USA.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Fassl
authors.affiliation: Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT, 84113, USA.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Maloney
authors.affiliation: Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT, 84113, USA.
authors.forename: Per H
authors.initials: PH
authors.lastname: Gesteland
authors.affiliation: Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT, 84108, USA.
authors.forename: Sashidhar R
authors.initials: SR
authors.lastname: Yerram
authors.affiliation: Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT, 84113, USA.
authors.forename: Flory L
authors.initials: FL
authors.lastname: Nkoy
mesh_terms: D000293:Adolescent; D001249:Asthma / Q000175:diagnosis*; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D011379:Prognosis; D012680:Sensitivity and Specificity