№1, 2025
RESEARCH OF THE INFLUENCE OF ECOLOGICAL FACTORS ON BRONCHIAL ASTHMA CRISIS USING ARTIFICIAL INTELLIGENCE METHODS
When examining the dynamics of inflammatory allergic upper respiratory diseases, including the factors affecting the onset of crises in bronchial asthma patients in particular, it becomes clear that the severity and duration of the situation, which reduces the quality of life and work capacity, directly depends on the characteristics of the environmental environment in which the patient is, i.e., allergens in the air, air pollution, industrial emissions and humidity coefficient, and factors such as observed hot weather conditions. The above-listed factors, functions, and constant coefficients were included in the mathematical model established to measure the severity of the crisis in bronchial asthma patients. The continuous coefficients representing the degrees of impact were determined based on the established a fully connected feedforward deep neural network model, and an approximate solution of the resulting system of differential equations was found using the Runge-Kutta 4th order method (pp.3-10).
Keywords: bronchial asthma, systems of differential equations, artificial intelligence, Runge-Kutta 4th order method, neural network
References
- Anderson, H. R., & Spix, C. (1997). Air pollution and daily admissions for chronic obstructive pulmonary disease in three European cities. European Respiratory Journal, 10(3), 487–493.
https://doi.org/10.1183/09031936.97.10030487 - Anderson, H. R., Favarato, G., & Atkinson, R. W. (2013). Long-term exposure to air pollution and the incidence of asthma: meta-analysis of cohort studies. Air Quality, Atmosphere & Health, 6(1), 47–56.
https://doi.org/10.1007/s11869-011-0144-5 - Arundel, A. V., Sterling, E. M., Biggin, J. H., & Sterling, T. D. (1986). Indirect health effects of relative humidity in indoor environments. Environmental Health Perspectives, 65, 351–361. https://doi.org/10.1289/ehp.8665351
- Ascher, U. M., & Petzold, L. R. (1998). Computer methods for ordinary differential equations and differential-algebraic equations. SIAM.
https://doi.org/10.1137/1.9781611971392 - Baldacci, S., Maio, S., Cerrai, S., Sarno, G., & Viegi, G. (2015). Allergy and asthma: Effects of the exposure to particulate matter and biological allergens. Respiratory Medicine, 109(9), 1089–1104.
https://doi.org/10.1016/j.rmed.2015.05.017 - Barnett, A. G., & Williams, G. M. (2006). Air pollution and asthma: Time series approaches. Proceedings of the American Thoracic Society, 3(3), 356–360.
https://doi.org/10.1513/pats.200511-124JH - Bousquet, J., et al. (2010). Uniform definition of asthma severity, control, and exacerbations: document presented for the World Health Organization Consultation on Severe Asthma. Journal of Allergy and Clinical Immunology, 126(5), 926–938. https://doi.org/10.1016/j.jaci.2010.07.019
- Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242. https://doi.org/10.1016/S0140-6736(02)11274-8
- Calderón, M. A., et al. (2016). Respiratory allergy caused by house dust mites: What do we really know? Journal of Allergy and Clinical Immunology, 136(1), 38–48. https://doi.org/10.1016/j.jaci.2015.05.025
- Custovic, A., & Simpson, A. (2012). Environmental allergen exposure, sensitisation and asthma: from whole populations to individuals at risk. Thorax, 67(6), 477–479.
https://doi.org/10.1136/thoraxjnl-2011-201058 - Custovic, A., & Woodcock, A. (2014). Environmental factors influencing asthma. The Lancet Respiratory Medicine, 2(2), 171–184. https://doi.org/10.1016/S2213-2600(13)70148-0
- D'Amato, G., et al. (2015). Effects on asthma and respiratory allergy of climate change and air pollution. Multidisciplinary Respiratory Medicine, 10(1), 39.
https://doi.org/10.1186/s40248-015-0036-x - D'Amato, G., Liccardi, G., D'Amato, M., & Holgate, S. (2005). Environmental risk factors and allergic bronchial asthma. Clinical & Experimental Allergy, 35(9), 1113–1124.
https://doi.org/10.1111/j.1365-2222.2005.02328.x - Guarnieri, M., & Balmes, J. R. (2014). Outdoor air pollution and asthma. The Lancet, 383(9928), 1581–1592.
https://doi.org/10.1016/S0140-6736(14)60617-6 - Hairer, E., Nørsett, S. P., & Wanner, G. (1993). Solving ordinary differential equations I: Nonstiff problems (2nd ed.). Springer.
https://doi.org/10.1007/978-3-540-78862-1 - Holgate, S. T. (2012). Innate and adaptive immune responses in asthma. Nature Medicine, 18(5), 673–683.
https://doi.org/10.1038/nm.2731 - Iserles, A. (2008). A first course in the numerical analysis of differential equations. Cambridge University Press.
https://doi.org/10.1017/CBO9781139107059 - Lotfata, A., Moosazadeh, M., Helbich, M., et al. (2023). Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests. International Journal of Health Geographics, 22, 18.
https://doi.org/10.1186/s12942-023-00343-6 - McConnell, R., et al. (2002). Asthma in exercising children exposed to ozone: A cohort study. The Lancet, 359(9304), 386–391.
https://doi.org/10.1016/S0140-6736(02)07522-8 - Papi, A., Brightling, C., Pedersen, S. E., & Reddel, H. K. (2018). Asthma. The Lancet, 391(10122), 783–800.
https://doi.org/10.1016/S0140-6736(17)33311-1 - Sak, H., Senior, A., & Beaufays, F. (2014). Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint. https://arxiv.org/abs/1402.1128
- Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
https://doi.org/10.1016/j.physd.2019.132306 - Stieb, D. M., Judek, S., & Burnett, R. T. (2002). Meta-analysis of time-series studies of air pollution and mortality: Effects of gases and particles. Environmental Health Perspectives, 110(7), 767–772.
https://doi.org/10.1289/ehp.02110767 - Zhang, A. M., Banzon, T. M., & Phipatanakul, W. (2023). The spectrum of environmental disparities in asthma. Journal of Allergy and Clinical Immunology, 153(2), 398–400.
https://doi.org/10.1016/j.jaci.2023.03.014
