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Effectiveness of cascading time series models based on meteorological factors in improving health risk prediction

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Abstract

Meteorological factors, which are periodic and regular in a long run, have an unignorable impact on human health. Accurate health risk prediction based on meteorological factors is essential for optimal allocation of resource in healthcare units. However, due to the non-stationary and non-linear nature of the original hospitalization sequence, traditional methods are less robust in predicting it. This study aims to investigate hospital admission prediction models using time series pre-processing algorithms and deep learning approach based on meteorological factors. Using the electronic medical record data from Panyu Central Hospital and meteorological data of Panyu district from 2003 to 2019, 46,089 eligible patients with lower respiratory tract infections (LRTIs) and four meteorological factors were identified to build and evaluate the prediction models. A novel hybrid model, Cascade GAM-CEEMDAN-LSTM Model (CGCLM), was established in combination with generalized additive model (GAM), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and long-short term memory (LSTM) networks for predicting daily admissions of patients with LRTIs. The experimental results show that CGCLM multistep method proposed in this paper outperforms single LSTM model in the prediction of health risk time series at different time window sizes. Moreover, our results also indicate that CGCLM has the best prediction performance when the time window is set to 61 days (RMSE = 1.12, MAE = 0.87, R2 = 0.93). Adequate extraction of exposure-response relationships between meteorological factors and diseases and suitable handling of sequence pre-processing have an important role in time series prediction. This hybrid climate-based model for predicting LRTIs disease can also be extended to time series prediction of other epidemic disease.

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References

  • Armstrong JS, Collopy F (1992) Error measures for generalizing about forecasting methods: empirical comparisons. Int J Forecast 8(1):69–80

    Google Scholar 

  • Darniot M, Pitoiset C, Millière L, Aho-Glélé LS, Florentin E, Bour JB et al (2018) Different meteorological parameters influence metapneumovirus and respiratory syncytial virus activity. J Clin Virol S1386653218301252

  • Das K, Nath D, Pradhan S (2020) FPGA and ASIC realization of EMD algorithm for real-time signal processing. IET Circuits Devices & Systems

  • Davidson MW, Haim DA, Radin JMJSR (2015) Using networks to combine “big data” and traditional surveillance to improve influenza predictions. 5:8154

  • Dong L, Fang D, Wang X, Wei W, Woniak M (2020) Prediction of streamflow based on dynamic sliding window LSTM. Water 12(11):3032

    Google Scholar 

  • Gao B, Huang X, Shi J, Tai Y, Zhang J (2020) Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renew Energy 162:1665–1683

    Google Scholar 

  • Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471

    CAS  Google Scholar 

  • Glick AF, Tomopoulos S, Fierman AH, Elixhauser A, Trasande L Association between outdoor air pollution levels and inpatient outcomes in pediatric pneumonia hospitalizations, 2007 to 2008. Acad Pediatr:2018

  • Gu J, Liang L, Song H, Kong Y, Ma R, Hou Y et al (2019) A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China. Sci Rep 9(1):17928

    Google Scholar 

  • Hans EW, V.H.M.a.H.P (2012) A framework for healthcare planning and control. Handbook of Healthcare System Scheduling

  • Hastie T, Tibshirani R (1987) Generalized additive models: some applications. Publ Am Stat Assoc 82(398):371–386

    Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    CAS  Google Scholar 

  • Huang D, Wu Z (2017) Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization. PLoS One 12(2):e0172539

    Google Scholar 

  • Huang N, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A 454:679–699

    Google Scholar 

  • Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688

    Google Scholar 

  • Iwasaki A, Foxman EF, Molony RD (2017) Early local immune defences in the respiratory tract. Nat Rev Immunol 17(1):7–20

    CAS  Google Scholar 

  • Jacinta, Chan, Phooi, M’ng, Mohammadali and One, M.J.P. Forecasting east Asian indices futures via a novel hybrid of wavelet-PCA denoising and artificial neural network models. 2016

  • Jinghong G, Yunzong S, Yaogui L, Liping L, Tang JW (2014) Impact of ambient humidity on child health: a systematic review. 9(12):e112508

  • Kadri F, Harrou F, Chaabane S, Tahon C (2014) Time series modelling and forecasting of emergency department overcrowding. J Med Syst 38(9):107

    Google Scholar 

  • Kassomenos P, Papaloukas C, Petrakis M, Karakitsios S (2008) Assessment and prediction of short term hospital admissions: the case of Athens, Greece. Atmos Environ 42(30):7078–7086

    CAS  Google Scholar 

  • Khaldi R, Afia AE, Chiheb R (2019) Forecasting of weekly patient visits to emergency department: real case study. Procedia Computer Science 148:532–541

    Google Scholar 

  • Khatri KL, Tamil LS (2018) Early detection of peak demand days of chronic respiratory diseases emergency department visits using artificial neural networks. IEEE Journal of Biomedical and Health Informatics 22(1):285–290

    Google Scholar 

  • Lampos V, De Bie T, Cristianini N (2010) Flu detector - tracking epidemics on twitter. In: Balcázar JL et al (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, pp 599–602

    Google Scholar 

  • Li D, Wang JB, Zhang ZY, Shen P, Chen K (2018) Effects of air pollution on hospital visits for pneumonia in children: a two-year analysis from China. Environmental ence & Pollution Research International 25(11):1–9

    Google Scholar 

  • Lin Y, Yan Y, Xu J, Liao Y, Ma F (2021) Forecasting stock index price using the CEEMDAN-LSTM model. The North American Journal of Economics and Finance 57:101421

    Google Scholar 

  • Linares C, Martinez GS, Kendrovski V, Diaz J (2020) A new integrative perspective on early warning systems for health in the context of climate change. Environ Res 187:109623

    CAS  Google Scholar 

  • Liu Y, Liu J, Chen F, Shamsi BH, Wang Q, Jiao F et al (2016) Impact of meteorological factors on lower respiratory tract infections in children. J Int Med Res 44(1):30–41

    Google Scholar 

  • Lowen AC, Mubareka S, Steel J, Palese P (2007) Influenza virus transmission is dependent on relative humidity and temperature. PLoS Pathog 3(10):1470–1476

    CAS  Google Scholar 

  • Luo L, Luo L, Zhang X, He X (2017) Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res 17(1):469

    Google Scholar 

  • Mäkinen TM, Juvonen R, Jokelainen J, Harju TH, Peitso A, Bloigu A et al (2009) Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections. Respir Med 103(3):456–462

    Google Scholar 

  • Meerhoff TJ, Paget JW, Kimpen JL, Schellevis FOJPIDJ (2009) Variation of respiratory syncytial virus and the relation with meteorological factors in different winter seasons. 28(10):860

  • Navares R, Aznarte JL (2020) Deep learning architecture to predict daily hospital admissions. Neural Comput & Applic:1–10

  • Price RHM, Graham C, Ramalingam S (2019) Association between viral seasonality and meteorological factors. Sci Rep 9(1):929

    Google Scholar 

  • Qiu H, Luo L, Su Z, Zhou L, Wang L, Chen Y (2020) Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure. BMC Med Inform Decis Mak 20(1):83

    Google Scholar 

  • Ramadona AL, Lazuardi L, Hii YL, Holmner Å, Kusnanto H, Rocklöv J (2016) Prediction of dengue outbreaks based on disease surveillance and meteorological data. PLoS One 11(3):e0152688–e0152688

    Google Scholar 

  • Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Phys Heart Circ Phys 278(6):H2039–H2049

    CAS  Google Scholar 

  • Ruchiraset A, Tantrakarnapa K (2018) Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand. Springer Open Choice 25(33)

  • Sahni S, Talwar A, Khanijo S, Talwar A (2017) Socioeconomic status and its relationship to chronic respiratory disease. Adv Respir Med 85(2):97–108

    Google Scholar 

  • Sainani KL (2014) Explanatory versus predictive Modeling. Pm & R 6(9):841–844

    Google Scholar 

  • Shaman J, Kohn M (2009) Absolute humidity modulates influenza survival, transmission, and seasonality. Proc Natl Acad Sci U S A 106(9):3243–3248

    CAS  Google Scholar 

  • Sharifi S, Saberi K (2014) Capacity planning in hospital management: an overview. 4

  • She W, Jia S, Hua Y, Feng X, Xing Y, She W et al (2021) The effect of nitrogen dioxide and atmospheric pressure on hospitalization risk for chronic obstructive pulmonary disease in Guangzhou, China. Respir Med 182:106424

    Google Scholar 

  • Song C, Fu X (2020) Research on different weight combination in air quality forecasting models. J Clean Prod 261:121169

    CAS  Google Scholar 

  • Soyiri IN, Reidpath DD (2012) Evolving forecasting classifications and applications in health forecasting. Int J Gen Med 5:381–389

    Google Scholar 

  • Soyiri IN, Reidpath DD (2013) An overview of health forecasting. Environmental Health & Preventive Medicine 18(1):1–9

    Google Scholar 

  • Suminski RR, Poston WC, Market P, Hyder M, Sara PA (2008) Meteorological conditions are associated with physical activities performed in open-air settings. Int J Biometeorol 52(3):189–197

    Google Scholar 

  • Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4144–4147

    Google Scholar 

  • Trachtenberg AJ, Dik N, Chateau D, Katz A (2014) Inequities in ambulatory care and the relationship between socioeconomic status and respiratory hospitalizations: a population-based study of a Canadian city. Ann Fam Med 12(5):402–407

    Google Scholar 

  • Wang T, Zhang M, Yu Q, Zhang H (2012) Comparing the application of EMD and EEMD on time-frequency analysis of seimic signal. J Appl Geophys 83:29–34

    Google Scholar 

  • Watts N, Amann M, Arnell N, Ayeb-Karlsson S, Beagley J, Belesova K et al (2020) The 2020 report of the lancet countdown on health and climate change: responding to converging crises. Lancet

  • WHO (2020a) 2019 global health estimates: the top 10 causes of death

  • WHO (2020b) Programmes and projects

  • Woodhead M, Blasi F, Ewig S, Garau J, Huchon G, Ieven M et al (2011) Guidelines for the management of adult lower respiratory tract infections--summary. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases 17(Suppl 6):1–24

    Google Scholar 

  • Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41

    Google Scholar 

  • Yousefi M, Ferreira R, Yousefi M (2016) A modeling approach for daily patient visits forecasting in an emergency department. In: 5th international conference on engineering optimization - Iguassu falls, Brazil, 19–23 June 2016

    Google Scholar 

  • Zhang X, Pang Y, Cui M, Stallones L, Xiang H (2015) Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Ann Epidemiol 25(2):101–106

    CAS  Google Scholar 

  • Zhou L, Zhao P, Wu D, Cheng C, Huang H (2018) Time series model for forecasting the number of new admission inpatients. BMC Med Inform Decis Mak 18(1):39

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the support provided by the High Performance Computing Center, China Pharmaceutical University.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (grant number 81773806, 81874331) and the Double-Class University project (grant numbers CPU2018GY19).

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Author notes

  1. Shuopeng Jia and Weibin She contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, #639 Longmian Avenue, Jiangning District, 211198, Nanjing, Jiangsu Province, China

    Shuopeng Jia, Xihan Lin & Mingjun Xu

  2. Medical Affairs, Science and Education Department, Foshan Fosun Chancheng Hospital, #3 Sanyou South Road, Chancheng District, Foshan, Guangdong Province, 52800, China

    Weibin She

  3. School of Pharmacy, China Pharmaceutical University, #639 Longmian Avenue, Jiangning District, Nanjing, Jiangsu Province, 211198, China

    Zhipeng Pi

  4. School of Science, China Pharmaceutical University, #639 Longmian Avenue, Jiangning District, Nanjing, Jiangsu Province, 211198, China

    Buying Niu & Jun Liao

  5. Meteorological Bureau of Panyu District, #5 Landscape Avenue, Hengjiang village, Shatou Street, Panyu District, 511400, Guangzhou, Guangdong Province, China

    Jinhua Zhang & Weiya She

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  1. Shuopeng Jia
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Contributions

Shuopeng Jia: methodology, software, writing — original draft

Weibin She: conceptualization, resources, data curation

Zhipeng Pi: software, writing — review and editing

Buying Niu: software

Jinhua Zhang: resources, investigation

Xihan Lin: writing — review and editing

Mingjun Xu: data curation

Weiya She: resources

Jun Liao: conceptualization, supervision, funding

Corresponding author

Correspondence to Jun Liao.

Ethics declarations

Ethics approval

This study was originally approved by the Clinical Research Ethics Committee of the Panyu center hospital with code [2020]25.

Consent to participate

This is a retrospective study; therefore, no patients actually participated in this study. The information collected in this study includes the date of admission, gender, age, and ICD number and does not include sensitive information such as the patient’s name. The patients’ personal information was adequately protected.

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All authors consent to publish this article in Environmental Science and Pollution Research.

Competing interests

The authors declare no competing interests.

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Jia, ., She, W., Pi, Z. et al. Effectiveness of cascading time series models based on meteorological factors in improving health risk prediction. Environ Sci Pollut Res 29, 9944–9956 (2022). https://doi.org/10.1007/s11356-021-16372-2

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