Roemer M. Health Care Expenditures for the Five Most Common Children’s Conditions, 2008: Estimates for U.S. Civilian Noninstitutionalized Children, Ages 0–17. MEPS Statistical Brief #349. AHRQ: Rockville, MD; 2011.
Malveaux FJ. The state of childhood asthma: introduction. Pediatrics. 2009;123 Suppl 3:S129–30.
Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland. JAMA. 1992;268(17):2388–94.
Akinbami LJ, Moorman JE, Liu X. Asthma prevalence, health care use, and mortality: United States, 2005–2009. Natl Health Stat Report. 2011;32:1–14.
Akinbami LJ, Moorman JE, Bailey C, Zahran HS, King M, Johnson CA, et al. Trends in asthma prevalence, health care use, and mortality in the United States, 2001–2010. NCHS Data Brief. 2012;94:1–8.
Vargas PA, Simpson PM, Bushmiaer M, Goel R, Jones CA, Magee JS, et al. Symptom profile and asthma control in school-aged children. Ann Allergy Asthma Immunol. 2006;96(6):787–93.
Zeiger RS, Yegin A, Simons FE, Haselkorn T, Rasouliyan L, Szefler SJ, et al. Evaluation of the National Heart, Lung, and Blood Institute guidelines impairment domain for classifying asthma control and predicting asthma exacerbations. Ann Allergy Asthma Immunol. 2012;108(2):81–7.
Wang LY, Zhong Y, Wheeler L. Direct and indirect costs of asthma in school-age children. Prev Chronic Dis. 2005;2(1):A11.
Mitchell EA, Bland JM, Thompson JM. Risk factors for readmission to hospital for asthma in childhood. Thorax. 1994;49(1):33–6.
Chapman KR, Boulet LP, Rea RM, Franssen E. Suboptimal asthma control: prevalence, detection and consequences in general practice. Eur Respir J. 2008;31(2):320–5.
Stempel DA, McLaughin TP, Stanford RH, Fuhlbrigge AL. Patterns of asthma control: a 3-year analysis of patient claims. J Allergy Clin Immunol. 2005;115(5):935–9.
Calhoun WJ, Sutton LB, Emmett A, Dorinsky PM. Asthma variability in patients previously treated with β2-agonists alone. J Allergy Clin Immunol. 2003;112(6):1088–94.
Zhang J, Yu C, Holgate ST, Reiss TF. Variability and lack of predictive ability of asthma end-points in clinical trials. Eur Respir J. 2002;20(5):1102–9.
Robroeks CM, van Vliet D, Jöbsis Q, Braekers R, Rijkers GT, Wodzig WK, et al. Prediction of asthma exacerbations in children: results of a one-year prospective study. Clin Exp Allergy. 2012;42(5):792–8.
Rabe KF, Adachi M, Lai CK, Soriano JB, Vermeire PA, Weiss KB, et al. Worldwide severity and control of asthma in children and adults: the global asthma insights and reality surveys. J Allergy Clin Immunol. 2004;114(1):40–7.
Davis KJ, Disantostefano R, Peden DB. Is Johnny wheezing? Parent–child agreement in the childhood asthma in America survey. Pediatr Allergy Immunol. 2011;22(1 pt 1):31–5.
Halterman JS, Yoos HL, Kitzman H, Anson E, Sidora-Arcoleo K, McMullen A. Symptom reporting in childhood asthma: a comparison of assessment methods. Arch Dis Child. 2006;91(9):766–70.
Nathan RA, Sorkness CA, Kosinski M, Schatz M, Li JT, Marcus P, et al. Development of the asthma control test: a survey for assessing asthma control. J Allergy Clin Immunol. 2004;113(1):59–65.
Zolnoori M, Zarandi MH, Moin M. Application of intelligent systems in asthma disease: designing a fuzzy rule-based system for evaluating level of asthma exacerbation. J Med Syst. 2012;36(4):2071–83.
Forno E, Celedón JC. Predicting asthma exacerbations in children. Curr Opin Pulm Med. 2012;18(1):63–9.
Lieu TA, Quesenberry CP, Sorel ME, Mendoza GR, Leong AB. Computer-based models to identify high-risk children with asthma. Am J Respir Crit Care Med. 1998;157(4 Pt 1):1173–80.
Lieu TA, Capra AM, Quesenberry CP, Mendoza GR, Mazar M. Computer-based models to identify high-risk adults with asthma: is the glass half empty of half full? J Asthma. 1999;36(4):359–70.
Frey U. Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model. Curr Opin Allergy Clin Immunol. 2007;7(3):223–30.
McCoy K, Shade DM, Irvin CG, Mastronarde JG, Hanania NA, Castro M, et al. Predicting episodes of poor asthma control in treated patients with asthma. J Allergy Clin Immunol. 2006;118(6):1226–33.
Nkoy FL, Stone BL, Fassl BA, Uchida DA, Koopmeiners K, Halbern S, et al. Longitudinal validation of a tool for asthma self-monitoring. Pediatrics. 2013;132(6):e1554–61.
Air quality data homepage of the United States Environmental Protection Agency. http://www.epa.gov/airdata/. Accessed Jan. 27, 2015.
MesoWest homepage. http://mesowest.utah.edu/. Accessed Jan. 27, 2015.
Respiratory virus surveillance homepage of GermWatch. https://intermountainphysician.org/gw/respiratoryviruses/Pages/default.aspx. Accessed Jan. 27, 2015.
Pollen count data homepage of Intermountain Allergy & Asthma. http://www.intermountainallergy.com/pollen.html. Accessed Jan. 27, 2015.
Evans RS, Lloyd JF, Pierce LA. Clinical use of an enterprise data warehouse. AMIA Annu Symp Proc. 2012;2012:189–98.
Bloomberg GR, Banister C, Sterkel R, Epstein J, Bruns J, Swerczek L, et al. Socioeconomic, family, and pediatric practice factors that affect level of asthma control. Pediatrics. 2009;123(3):829–35.
Cope SF, Ungar WJ, Glazier RH. Socioeconomic factors and asthma control in children. Pediatr Pulmonol. 2008;43(8):745–52.
Schatz M, Sorkness CA, Li JT, Marcus P, Murray JJ, Nathan RA, et al. Asthma control test: reliability, validity, and responsiveness in patients not previously followed by asthma specialists. J Allergy Clin Immunol. 2006;117(3):549–56.
Kuhn M, Johnson K. Applied Predictive Modeling. New York: Springer; 2013.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-sampling Technique. J Artif Intell Res. 2002;16:321–57.
Box GEP, Cox DR. An analysis of transformations. J R Stat Soc Ser B. 1964;26:211–52.
Witten IH, Frank E, Hall MA. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed. Burlington, MA: Morgan Kaufmann; 2011.
Wu X, Kumar V. The Top Ten Algorithms in Data Mining. Chapman & Hall/CRC: Boca Raton, FL; 2009.
Deng L, Yu D. Deep learning: methods and applications. Foundations and Trends in Signal Processing. 2014;7(3–4):197–387.
Deepnet package in R homepage. http://cran.r-project.org/web/packages/deepnet/index.html. Accessed Jan. 27, 2015.
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54.
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504–7.
Nkoy FL, Stone BL, Fassl BA, Koopmeiners K, Halbern S, Kim EH, et al. Development of a novel tool for engaging children and parents in asthma self-management. AMIA Annu Symp Proc. 2012;2012:663–72.
Haselkorn T, Fish JE, Zeiger RS, Szefler SJ, Miller DP, Chipps BE, et al. Consistently very poorly controlled asthma, as defined by the impairment domain of the Expert Panel Report 3 guidelines, increases risk for future severe asthma exacerbations in The Epidemiology and Natural History of Asthma: Outcomes and Treatment Regimens (TENOR) study. J Allergy Clin Immunol. 2009;124(5):895–902.
Lee CH, Chen JC, Tseng VS. A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring. Comput Methods Programs Biomed. 2011;101(1):44–61.
Delfino RJ, Staimer N, Tjoa T, Gillen D, Kleinman MT, Sioutas C, et al. Personal and ambient air pollution exposures and lung function decrements in children with asthma. Environ Health Perspect. 2008;116(4):550–8.
Gautrin D, D'Aquino LC, Gagnon G, Malo JL, Cartier A. Comparison between peak expiratory flow rates (PEFR) and FEV1 in the monitoring of asthmatic subjects at an outpatient clinic. Chest. 1994;106(5):1419–26.
Frischer T, Meinert R, Urbanek R, Kuehr J. Variability of peak expiratory flow rate in children: short and long term reproducibility. Thorax. 1995;50(1):35–9.
Adeniyi A, Erhabor G. The peak flow meter and its use in clinical practice. Afr J Respir Med. 2011;6(2):5–7.
Goldberg S, Springer C, Avital A, Godfrey S, Bar-Yishay E. Can peak expiratory flow measurements estimate small airway function in asthmatic children? Chest. 2001;120(2):482–8.
Carson JW, Hoey H, Taylor MR. Growth and other factors affecting peak expiratory flow rate. Arch Dis Child. 1989;64(1):96–102.
Walter MJ, Castro M, Kunselman SJ, Chinchilli VM, Reno M, Ramkumar TP, et al. Predicting worsening asthma control following the common cold. Eur Respir J. 2008;32(6):1548–54.
Schatz M. Predictors of asthma control: what can we modify? Curr Opin Allergy Clin Immunol. 2012;12(3):263–8.
Delfino RJ, Quintana PJ, Floro J, Gastañaga VM, Samimi BS, Kleinman MT, et al. Association of FEV1 in asthmatic children with personal and microenvironmental exposure to airborne particulate matter. Environ Health Perspect. 2004;112(8):932–41.
Keeler GJ, Dvonch T, Yip FY, Parker EA, Isreal BA, Marsik FJ, et al. Assessment of personal and community-level exposures to particulate matter among children with asthma in Detroit, Michigan, as part of Community Action Against Asthma (CAAA). Environ Health Perspect. 2002;110 Suppl 2:173–81.
Delfino RJ, Coate BD, Zeiger RS, Seltzer JM, Street DH, Koutrakis P. Daily asthma severity in relation to personal ozone exposure and outdoor fungal spores. Am J Respir Crit Care Med. 1996;154(3 Pt 1):633–41.
Maestrelli P, Canova C, Scapellato ML, Visentin A, Tessari R, Bartolucci GB, et al. Personal exposure to particulate matter is associated with worse health perception in adult asthma. J Investig Allergol Clin Immunol. 2011;21(2):120–8.
Dick S, Doust E, Cowie H, Ayres JG, Turner S. Associations between environmental exposures and asthma control and exacerbations in young children: a systematic review. BMJ Open. 2014;4(2):e003827.
Jackson DJ, Johnston SL. The role of viruses in acute exacerbations of asthma. J Allergy Clin Immunol. 2010;125(6):1178–87.
Velicer WF, Fava JL. Time series analysis for psychological research. In: Weiner IB, Schinka JA, Velicer WF, editors. Handbook of Psychology, Research Methods in Psychology (Volume 2). Hoboken, NJ: Wiley; 2012.