Abstract
Bronchopneumonia is the most common infectious disease in children, and it seriously endangers children’s health. In this paper, a deep neural network combining long short-term memory (LSTM) layers and fully connected layers was proposed to predict the prevalence of bronchopneumonia in children in Chengdu based on environmental factors and previous prevalence rates. The mean square error (MSE), mean absolute error (MAE), and Pearson correlation coefficient (R) were used to detect the performance of the deep learning model. The values of MSE, MAE, and R in the test dataset are 0.0051, 0.053, and 0.846, respectively. The results show that the proposed model can accurately predict the prevalence of bronchopneumonia in children. We also compared the proposed model with three other models, namely, a fully connected (FC) layer neural network, a random forest model, and a support vector machine. The results show that the proposed model achieves better performance than the three other models by capturing time series and mitigating the lag effect.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Antonio Ortega-Garcia J, Martinez-Hernandez I, Boldo E, Carceles-Alvarez A, Solano-Navarro C, Ramis R et al (2020) Urban air pollution and hospital admissions for asthma and acute respiratory disease in Murcia city (Spain). Anales De Pediatria 93(2):95–102. https://doi.org/10.1016/j.anpedi.2020.01.012
Bai L, Chen H, Hatzopoulou M, Jerrett M, Kwong JC, Burnett RT, van Donkelaar A, Copes R, Martin RV, van Ryswyk K, Lu H, Kopp A, Weichenthal S (2018) Exposure to ambient ultrafine particles and nitrogen dioxide and incident hypertension and diabetes. Epidemiology 29(3):323–332. https://doi.org/10.1097/ede.0000000000000798
Cabaneros SM, Calautit JK, Hughes B (2020) Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique. Ecol Model 424:109017. https://doi.org/10.1016/j.ecolmodel.2020.109017
Chang ME, Cardelino C (2000) Application of the urban airshed model to forecasting next-day peak ozone concentrations in Atlanta, Georgia. J Air Waste Manage Assoc 50(11):2010–2024. https://doi.org/10.1080/10473289.2000.10464219
Chang YS, Chiao HT, Abimannan S, Huang YP, Tsai YT, Lin KM (2020) An LSTM-based aggregated model for air pollution forecasting. Atmos Pollut Res 11(8):1451–1463. https://doi.org/10.1016/j.apr.2020.05.015
Chengdu Environmental Quality Bulletin in 2017. Available from https://www.sohu.com/a/232144579_120237. [Accessed 15 December 2020].
Chinese Meteorological Science Data Center. (n.d.) Availablle from: http://data.cma.cn// [Accessed 12 October 2020]
Ecological Environment Department of the Chengdu Bureau. (n.d.) Available from: http://sthj.chengdu.gov.cn. [Accessed 15 August 2020].
Faustini A, Stafoggia M, Colais P, Berti G, Bisanti L, Cadum E et al (2013) Air pollution and multiple acute respiratory outcomes. Eur Respir J 42(2):304–313. https://doi.org/10.1183/09031936.00128712
Feng C, Li J, Sun WJ, Zhang Y, Wang QY (2016) Impact of ambient fine particulate matter (PM2.5) exposure on the risk of influenza-like-illness: a time-series analysis in Beijing, China. Environ Health 15:17. https://doi.org/10.1186/s12940-016-0115-2
Gayen A, Pourghasemi HR, Saha S, Keesstra S, Bai SB (2019) Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci Total Environ 668:124–138. https://doi.org/10.1016/j.scitotenv.2019.02.436
Gehring U, Gruzieva O, Agius RM, Beelen R, Custovic A, Cyrys J, Eeftens M, Flexeder C, Fuertes E, Heinrich J, Hoffmann B, de Jongste JC, Kerkhof M, Klümper C, Korek M, Mölter A, Schultz ES, Simpson A, Sugiri D, Svartengren M, von Berg A, Wijga AH, Pershagen G, Brunekreef B (2013) Air pollution exposure and lung function in children: the ESCAPE project. Environ Health Perspect 121(11-12):1357–1364. https://doi.org/10.1289/ehp.1306770
Ghimire B, Rogan J, Miller J (2010) Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sensing Lett 1(1):45–54. https://doi.org/10.1080/01431160903252327
Goldizen FC, Sly PD, Knibbs LD (2016) Respiratory effects of air pollution on children. Pediatr Pulmonol 51(1):94–108. https://doi.org/10.1002/ppul.23262
Haryanto B (2020) Indonesia: country report on children’s environmental health. Rev Environ Health 35(1):41–48. https://doi.org/10.1515/reveh-2019-0088
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang C-J, Kuo P-H (2018) A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities. Sensors 18(7). https://doi.org/10.3390/s18072220
Huang Z-Q, Chen Y-C, Wen C-Y (2020) Real-time weather monitoring and prediction using city buses and machine learning. Sensors 20(18). https://doi.org/10.3390/s20185173
Jiang T, Chen B, Nie Z, Ren Z, Xu B, Tang S (2021) Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model. Atmos Res 248. https://doi.org/10.1016/j.atmosres.2020.105146
Kavzoglu T, Sahin E, Colkesen I (2013) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439. https://doi.org/10.1007/s10346-013-0391-7
Khanam A, Ul Abadeen Z, Mushtaq R (2018) Frequency of bronchopneumonia in patients admitted at D.H.Q Teaching Hospital Sargodha. Pakistan J Med Health Sci 12(2):679–680
Kuo C-Y, Chan C-K, Wu C-Y, Dinh-Van P, Chan C-L (2019) The short-term effects of ambient air pollutants on childhood asthma hospitalization in Taiwan: a national study. Int J Environ Res Public Health 16(2). https://doi.org/10.3390/ijerph16020203
Landrigan PJ, Fuller R, Acosta NJR, Adeyi O, Arnold R, Basu N et al (2018) The Lancet Commission on pollution and health. Lancet 391(10119):462–512. https://doi.org/10.1016/s0140-6736(17)32345-0
Landscan Global Population Distribution Data. (n.d.) Available from: https://landscan.ornl.gov/. [Accessed 8 September 2020]
Liu Y, Guo Y, Wang C, Li W, Lu J, Shen S, Xia H, He J, Qiu X (2015) Association between temperature change and outpatient visits for respiratory tract infections among children in Guangzhou, China. Int J Environ Res Public Health 12(1):439–454. https://doi.org/10.3390/ijerph120100439
Liu C, Chen R, Sera F, Vicedo-Cabrera AM, Guo YM, Tong SL et al (2019) Ambient particulate air pollution and daily mortality in 652 cities. N Engl J Med 381(8):705–715. https://doi.org/10.1056/NEJMoa1817364
Maragatham G, Devi S (2019) LSTM model for prediction of heart failure in big data. J Med Syst 43(5):111. https://doi.org/10.1007/s10916-019-1243-3
Mazzoni D, Garay MJ, Davies R, Nelson D (2007) An operational MISR pixel classifier using support vector machines. Remote Sens Environ 107(1):149–158. https://doi.org/10.1016/j.rse.2006.06.021
Meher G, Bhattacharjya S, Chakraborty H (2019) Membrane cholesterol modulates oligomeric status and peptide-membrane interaction of severe acute respiratory syndrome coronavirus fusion peptide. J Phys Chem B 123(50):10654–10662. https://doi.org/10.1021/acs.jpcb.9b08455
Nathan AM, Teh CSJ, Eg KP, Jabar KA, Zaki R, Hng SY, Westerhout C, Thavagnanam S, de Bruyne JA (2020) Respiratory sequelae and quality of life in children one-year after being admitted with a lower respiratory tract infection: a prospective cohort study from a developing country. Pediatr Pulmonol 55(2):407–417. https://doi.org/10.1002/ppul.24598
Navares R, Aznarte JL (2020) Predicting air quality with deep learning LSTM: towards comprehensive models. Ecol Inform 55:101019. https://doi.org/10.1016/j.ecoinf.2019.101019
Niu N, Liu X, Jin H, Ye X, Liu Y, Li X, Chen Y, Li S (2017) Integrating multi-source big data to infer building functions. Int J Geogr Inf Sci 31(9):1871–1890. https://doi.org/10.1080/13658816.2017.1325489
Payne-Sturges DC, Marty MA, Perera F, Miller MD, Swanson M, Ellickson K, Cory-Slechta DA, Ritz B, Balmes J, Anderko L, Talbott EO, Gould R, Hertz-Picciotto I (2019) Healthy air, healthy brains: advancing air pollution policy to protect children’s health. Am J Public Health 109(4):550–554. https://doi.org/10.2105/ajph.2018.304902
Pride KR, Peel JL, Robinson BF, Busacker A, Grandpre J, Bisgard KM, Yip FY, Murphy TD (2015) Association of short-term exposure to ground-level ozone and respiratory outpatient clinic visits in a rural location - Sublette County, Wyoming, 2008-2011. Environ Res 137:1–7. https://doi.org/10.1016/j.envres.2014.10.033
Rasp S, Lerch S (2018) Neural networks for postprocessing ensemble weather forecasts. Mon Weather Rev 146(11):3885–3900. https://doi.org/10.1175/mwr-d-18-0187.1
Sarria EE, Mundstock E, Mocelin HT, Fischer GB, Torres RR, Garbin JGM, Leal LF, de F. Arend MHR, Stein R, Booij L, de Araújo RMF, Mattiello R (2019) Health-related quality of life in post-infectious bronchiolitis obliterans: agreement between children and their proxy. J Pediatr 95(5):614–618. https://doi.org/10.1016/j.jped.2018.05.014
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, Chen Y, Lillicrap T, Hui F, Sifre L, van den Driessche G, Graepel T, Hassabis D (2017) Mastering the game of Go without human knowledge. Nature 550(7676):354–359. https://doi.org/10.1038/nature24270
Strak M, Boogaard H, Meliefste K, Oldenwening M, Zuurbier M, Brunekreef B, Hoek G (2010) Respiratory health effects of ultrafine and fine particle exposure in cyclists. Occup Environ Med 67(2):118–124. https://doi.org/10.1136/oem.2009.046847
Sun S, Tian L, Cao W, Lai P-C, Wong PPY, Lee, R. S.-y., … Wong, C.-M. (2019) Urban climate modified short-term association of air pollution with pneumonia mortality in Hong Kong. Sci Total Environ 646:618–624. https://doi.org/10.1016/j.scitotenv.2018.07.311
Tian L, Sun S (2017) Comparison of health impact of air pollution between China and other countries. Adv Exp Med Biol 1017:215–232. https://doi.org/10.1007/978-981-10-5657-4_9
Wang XH, Wang BZ (2019) Research on prediction of environmental aerosol and PM2.5 based on artificial neural network. Neural Comput Applic 31(12):8217–8227. https://doi.org/10.1007/s00521-018-3861-y
Wang C, Qi Y, Zhu G (2020) Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China. Chemosphere 257:127176. https://doi.org/10.1016/j.chemosphere.2020.127176
Yakubu Y, Ahmed SS, Audu I, Usman A (2019) Binary logistic regression methods for modeling broncho-pneumonia status in infants from tertiary health institutions in north central Nigeria. J Appl Sci Environ Manag 23(8):1607–1614. https://doi.org/10.4314/jasem.v23i8.28
Yang C-T, Chen Y-A, Chan Y-W, Lee C-L, Tsan Y-T, Chan W-C, Liu P-Y (2020) Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources. J Supercomput 76(12):9303–9329. https://doi.org/10.1007/s11227-020-03182-5
Yao XA, Huang H, Jiang B, Krisp JM (2019) Representation and analytical models for location-based big data. Int J Geogr Inf Sci 33(4):707–713. https://doi.org/10.1080/13658816.2018.1562068
Zeng YY, Cao YF, Qiao X, Seyler BC, Tang Y (2019) Air pollution reduction in China: recent success but great challenge for the future. Sci Total Environ 663:329–337. https://doi.org/10.1016/j.scitotenv.2019.01.262
Zhang J, Nawata K (2018) Multi-step prediction for influenza outbreak by an adjusted long short-term memory. Epidemiol Infect 146(7):809–816. https://doi.org/10.1017/s0950268818000705
Zhao J, Deng F, Cai Y, Chen J (2019a) Long short-term memory - fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. Chemosphere 220:486–492. https://doi.org/10.1016/j.chemosphere.2018.12.128
Zhao R, Yan RQ, Chen ZH, Mao KZ, Wang P, Gao RX (2019b) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237. https://doi.org/10.1016/j.ymssp.2018.05.050
Zhou H, Wang T, Zhou F, Liu Y, Zhao W, Wang X, Chen H, Cui Y (2019a) Ambient air pollution and daily hospital admissions for respiratory disease in children in Guiyang, China. Front Pediatr 7. https://doi.org/10.3389/fped.2019.00400
Zhou Y, Chang F-J, Chang L-C, Kao IF, Wang Y-S (2019b) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145. https://doi.org/10.1016/j.jclepro.2018.10.243
Zhu BZ, Pang RZ, Chevallier J, Wei YM, Vo DT (2019) Including intangible costs into the cost-of-illness approach: a method refinement illustrated based on the PM2.5 economic burden in China. Eur J Health Econ 20(4):501–511. https://doi.org/10.1007/s10198-018-1012-0
Zou B, You JW, Lin Y, Duan XL, Zhao XG, Fang X et al (2019) Air pollution intervention and life-saving effect in China. Environ Int 125:529–541. https://doi.org/10.1016/j.envint.2018.10.045
Zuniga J, Tarajia M, Herrera V, Urriola W, Gomez B, Motta J (2016) Assessment of the possible association of air pollutants PM10, O-3, NO2 with an increase in cardiovascular, respiratory, and diabetes mortality in Panama City A 2003 to 2013 data analysis. Medicine 95(2):e2464. https://doi.org/10.1097/md.0000000000002464
Acknowledgements
We would like to thank the editors and anonymous referees for their constructive comments.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Funding
The research was financially supported by the Fundamental Research Funds for the Central Universities (XDJK2019B008), and National Major Projects on High-Resolution Earth Obser-vation System (21-Y20B01-9001-19/22).
Author information
Authors and Affiliations
Contributions
Zhao, DZ analysed the data, constructed a prediction model for children with bronchopneumonia, and wrote the manuscript. Shen, JW puts forward the research objectives and ideas of the paper and was responsible for the planning and execution of research activities. Huang, Y was involved in the analysis of the data. Chen, M; Shi, KF; and Ma, MG offered some guidelines for the research.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Lotfi Aleya
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhao, D., Chen, M., Shi, K. et al. A long short-term memory-fully connected (LSTM-FC) neural network for predicting the incidence of bronchopneumonia in children . Environ Sci Pollut Res 28, 56892–56905 (2021). https://doi.org/10.1007/s11356-021-14632-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-14632-9