References

1. Hamburg MA, Collins FS. The Path to Personalized Medicine. N Engl J Med . 2010;363(4):301-304. doi:10.1056/NEJMp1006304
2. Saglani S, Custovic A. Childhood asthma: Advances using machine learning and mechanistic studies. Am J Respir Crit Care Med . 2019;199(4):414-422. doi:10.1164/rccm.201810-1956CI
3. Pavord ID, Beasley R, Agusti A, et al. After asthma: redefining airways diseases. Lancet . 2018;391(10118):350-400. doi:10.1016/S0140-6736(17)30879-6
4. Custovic A, Belgrave D, Lin L, et al. Cytokine responses to rhinovirus and development of asthma, allergic sensitization, and respiratory infections during childhood. Am J Respir Crit Care Med . 2018;197(10):1265-1274. doi:10.1164/rccm.201708-1762OC
5. Blais L, Kettani FZ, Lemire C, et al. Inhaled corticosteroids vs. leukotriene-receptor antagonists and asthma exacerbations in children.Respir Med . 2011;105(6):846-855. doi:10.1016/j.rmed.2010.12.007
6. Turkalj M, Erceg D. Terapijski pristup astmi u djece. Medicus . 2013;22(1):49-56.
7. Wu W, Bleecker E, Moore W, et al. Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data. J Allergy Clin Immunol . 2014;133(5):1280-1288. doi:10.1016/j.jaci.2013.11.042
8. Szefler SJ, Phillips BR, Martinez FD, et al. Characterization of within-subject responses to fluticasone and montelukast in childhood asthma. J Allergy Clin Immunol . 2005;115(2):233-242. doi:10.1016/j.jaci.2004.11.014
9. Chung KF, Adcock IM. Clinical Phenotypes of Asthma Should Link up with Disease Mechanisms . Vol 15. Lippincott Williams and Wilkins; 2015:56-62. doi:10.1097/ACI.0000000000000134
10. Belgrave D, Cassidy R, Stamate D, et al. Predictive modelling strategies to understand heterogeneous manifestations of asthma in early life. In: Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 . Vol 2017-December. Institute of Electrical and Electronics Engineers Inc.; 2017:68-75. doi:10.1109/ICMLA.2017.0-176
11. Ross MK, Yoon J, Van Der Schaar A, Van Der Schaar M. Discovering pediatric asthma phenotypes on the basis of response to controller medication using machine learning. Ann Am Thorac Soc . 2018;15(1):49-58. doi:10.1513/AnnalsATS.201702-101OC
12. Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention, 2020. ; 2020.
13. Reddel HK, Taylor DR, Bateman ED, et al. An official American Thoracic Society/European Respiratory Society statement: Asthma control and exacerbations - Standardizing endpoints for clinical asthma trials and clinical practice. Am J Respir Crit Care Med . 2009;180(1):59-99. doi:10.1164/rccm.200801-060ST
14. Dweik RA, Boggs PB, Erzurum SC, et al. An official ATS clinical practice guideline. Am J Respir Crit Care Med . 2011;184(5):602-615. doi:10.1164/rccm.9120-11ST T4 - interpretation of exhaled nitric oxide levels (FENO) for clinical applications PM - 21885636 M4 - Citavi
15. de Jongste JC. Yes to NO: The first studies on exhaled nitric oxide-driven asthma treatment. Eur Respir J . 2005;26(3):379-381. doi:10.1183/09031936.05.00080705
16. Smith AD, Cowan JO, Brassett KP, Herbison GP, Taylor DR. Use of Exhaled Nitric Oxide Measurements to Guide Treatment in Chronic Asthma.N Engl J Med . 2005;352(21):2163-2173. doi:10.1056/NEJMoa043596
17. Pellegrino R, Viegi G, Brusasco V, et al. Interpretative strategies for lung function tests. Eur Respir J . 2005;26(5):948-968. doi:10.1183/09031936.05.00035205
18. Žuvela P, Lovric M, Yousefian-Jazi A, Liu JJ. Ensemble Learning Approaches to Data Imbalance and Competing Objectives in Design of an Industrial Machine Vision System. Ind Eng Chem Res . 2020;59(10):4636-4645. doi:10.1021/acs.iecr.9b05766
19. Friedman JH. On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Min Knowl Discov . 1997;1(1):55-77. doi:10.1023/A:1009778005914
20. Sheehan WJ, Phipatanakul W. Indoor allergen exposure and asthma outcomes. Curr Opin Pediatr . 2016;28(6):772-777. doi:10.1097/MOP.0000000000000421
21. Lombardi C, Savi E, Ridolo E, Passalacqua G, Canonica GW. Is allergic sensitization relevant in severe asthma? Which allergens may be culprit? World Allergy Organ J . 2017;10(1). doi:10.1186/s40413-016-0138-8
22. Chicco D. Ten quick tips for machine learning in computational biology. BioData Min . 2017;10(1). doi:10.1186/s13040-017-0155-3
23. Chawla N V., Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-Sampling Technique . Vol 16.; 2002:321-357. doi:10.1613/jair.953
24. He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng . 2009;21(9):1263-1284. doi:10.1109/TKDE.2008.239
25. Zhang YP, Zhang LN, Wang YC. Cluster-based majority under-sampling approaches for class imbalance learning. In: Proceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010 . ; 2010:400-404. doi:10.1109/ICIFE.2010.5609385
26. Breiman L. Random forests. Mach Learn . 2001;45(1):5-32. doi:10.1023/A:1010933404324
27. Freund Y, Schapire RE. Experiments with a New Boosting Algorithm. In: Machine Learning : Proceedings of the Thirteenth International Conference . ; 1996:148-156.
28. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In:Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . Vol 13-17-August-2016. Association for Computing Machinery; 2016:785-794. doi:10.1145/2939672.2939785
29. Malva A, Arpinelli F, Recchia G, Micheletto C, Alexander R. Artificial Intelligence applied to asthma biomedical research: a systematic review. In: Medical Education, Web and Internet . European Respiratory Society; 2019:PA1482. doi:10.1183/13993003.congress-2019.PA1482
30. Havaš Auguštin D, Šarac J, Lovrić M, et al. Adherence to Mediterranean Diet and Maternal Lifestyle during Pregnancy: Island–Mainland Differentiation in the CRIBS Birth Cohort.Nutrients . 2020;12(8):2179. doi:10.3390/nu12082179
31. Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A. Conditional variable importance for random forests. BMC Bioinformatics . 2008;9:1-11. doi:10.1186/1471-2105-9-307
32. Šimić I, Lovrić M, Godec R, Kröll M, Bešlić I. Applying machine learning methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon.Environ Pollut . 2020;263:114587. doi:10.1016/j.envpol.2020.114587
33. Lučić B, Batista J, Bojović V, et al. Estimation of Random Accuracy and its Use in Validation of Predictive Quality of Classification Models within Predictive Challenges. Croat Chem Acta . 2019;92(3). doi:10.5562/cca3551
34. Chicco D, Rovelli C. Computational prediction of diagnosis and feature selection on mesothelioma patient health records. PLoS One . 2019;14(1). doi:10.1371/journal.pone.0208737
35. Groningen EZ, Kerstjens HAM, Brand PLP, Koeter GH, Postma DS, De Jong PM. Influence of treatment on peak expiratory flow and its relation to airway hyperresponsiveness and symptoms. Thorax . 1994;49(11):1109-1115. doi:10.1136/thx.49.11.1109
36. Brand PLP, Duiverman EJ, Waalkens HJ, Van Essen-Zandvliet EEM, Kerrebijn KF. Peak flow variation in childhood asthma: Correlation with symptoms, airways obstruction, and hyperresponsiveness during long term treatment with inhaled corticosteroids. Thorax . 1999;54(2):103-107. doi:10.1136/thx.54.2.103
37. Tonascia J, Adkinson NF, Bender B, et al. Long-Term Effects of Budesonide or Nedocromil in Children with Asthma. N Engl J Med . 2000;343(15):1054-1063. doi:10.1056/NEJM200010123431501
38. Smith AD, Cowan JO, Brassett KP, et al. Exhaled nitric oxide: A predictor of steroid response. Am J Respir Crit Care Med . 2005;172(4):453-459. doi:10.1164/rccm.200411-1498OC
39. Bagnasco D, Ferrando M, Varricchi G, Passalacqua G, Canonica GW. A critical evaluation of Anti-IL-13 and Anti-IL-4 strategies in severe asthma. Int Arch Allergy Immunol . 2016;170(2):122-131. doi:10.1159/000447692
40. Price D, Ryan D, Burden A, et al. Using fractional exhaled nitric oxide (FeNO) to diagnose steroid-responsive disease and guide asthma management in routine care. Clin Transl Allergy . 2013;3(1):1-10. doi:10.1186/2045-7022-3-37
41. Tharwat A. Classification assessment methods. Applied Computing and Informatics . 2018.
42. G. Weiss. Mining with rarity: A unifying framework. SIGKDD Explor . 2004;6(1):7-19. doi:10.1145/1007730.1007734
43. Froidure A, Mouthuy J, Durham SR, Chanez P, Sibille Y, Pilette C. Asthma phenotypes and IgE responses. Eur Respir J . 2016;47(1):304-319. doi:10.1183/13993003.01824-2014
44. Licari A, Castagnoli R, Brambilla I, et al. Asthma endotyping and biomarkers in childhood asthma. Pediatr Allergy, Immunol Pulmonol . 2018;31(2):44-55. doi:10.1089/ped.2018.0886
45. Fitzpatrick AM, Jackson DJ, Mauger DT, et al. Individualized therapy for persistent asthma in young children. J Allergy Clin Immunol . 2016;138(6):1608-1618.e12. doi:10.1016/j.jaci.2016.09.028
46. Monadi, Mahmoud; Firouzjahi, Alireza; Hosseini, Amin; Javadian, Yahya; Sharbatdaran, Majid; Heidari B. Serum C-reactive protein in asthma and its ability in predicting asthma control, a case-control study. Casp J Intern Med . 2016;7(1):37-42.
47. Torrego Fernández A, Muñoz Cano RM. [Clinical relevance of distal airway involvement in asthma]. Arch Bronconeumol . 2011;47 Suppl 2:17-19. doi:10.1016/S0300-2896(11)70016-3
48. Corren J. Small airways disease in asthma. Curr Allergy Asthma Rep . 2008;8(6):533-539. doi:10.1007/s11882-008-0097-4
49. Ye ZH, Huang Y, Wang Y, Wang DJ. Association between body mass index and lung function in children with asthma after corticosteroids inhalation. Chinese J Contemp Pediatr . 2013;15(11):983-986. doi:10.7499/j.issn.1008-8830.2013.11.014
50. Walker C, Bode E, Boer L, Hansel TT, Blaser K, Johann-Christian Virchow J. Allergic and Nonallergic Asthmatics Have Distinct Patterns of T-Cell Activation and Cytokine Production in Peripheral Blood and Bronchoalveolar Lavage. http://dx.doi.org/101164/ajrccm/1461109 . December 2012. doi:10.1164/AJRCCM/146.1.109
Table 1. The variables used in this study, described in more detail in the supplementary file. AR- allergic rhinitis, AD- atopic dermatitis, FVC- forced vital capacity, SPT- skin prick test, IgE- immunoglobulin E, ENT- ear/nose/throat, GLCCI1 - glucocorticoid induced 1,TBX21 - t-box 21, CRHR1 - corticotropin releasing hormone receptor 1, ADRB2 - beta-2 adrenergic receptor, MMP9 - matrix metalloproteinase-9.