IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i10p5959-d815382.html
   My bibliography  Save this article

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster

Author

Listed:
  • Corrado Lanera

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy)

  • Ileana Baldi

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy)

  • Andrea Francavilla

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy)

  • Elisa Barbieri

    (Division of Pediatric Infectious Diseases, Department of Women’s and Children’s Health, University of Padova, 35131 Padova, Italy)

  • Lara Tramontan

    (Consorzio Arsenàl.IT, 35131 Padova, Italy)

  • Antonio Scamarcia

    (Società Servizi Telematici–Pedianet, 35138 Padova, Italy)

  • Luigi Cantarutti

    (Società Servizi Telematici–Pedianet, 35138 Padova, Italy)

  • Carlo Giaquinto

    (Division of Pediatric Infectious Diseases, Department of Women’s and Children’s Health, University of Padova, 35131 Padova, Italy
    Società Servizi Telematici–Pedianet, 35138 Padova, Italy)

  • Dario Gregori

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy)

Abstract

The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children’s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.

Suggested Citation

  • Corrado Lanera & Ileana Baldi & Andrea Francavilla & Elisa Barbieri & Lara Tramontan & Antonio Scamarcia & Luigi Cantarutti & Carlo Giaquinto & Dario Gregori, 2022. "A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster," IJERPH, MDPI, vol. 19(10), pages 1-13, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5959-:d:815382
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/10/5959/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/10/5959/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qinneng Xu & Yulia R Gel & L Leticia Ramirez Ramirez & Kusha Nezafati & Qingpeng Zhang & Kwok-Leung Tsui, 2017. "Forecasting influenza in Hong Kong with Google search queries and statistical model fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    2. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    2. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    3. Monday Osayande & Osagie Osifo, 2024. "Application Of Covid-19 Data: Investigating The Impact On Weekly Stock Market Returns In Nigeria," Journal of Academic Research in Economics, Spiru Haret University, Faculty of Accounting and Financial Management Constanta, vol. 16(2 (July)), pages 403-416.
    4. Fan Li & Hao Zhou & De-Sheng Huang & Peng Guan, 2020. "Global Research Output and Theme Trends on Climate Change and Infectious Diseases: A Restrospective Bibliometric and Co-Word Biclustering Investigation of Papers Indexed in PubMed (1999–2018)," IJERPH, MDPI, vol. 17(14), pages 1-14, July.
    5. Jungyoon Kim & Jihye Lim, 2021. "A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    6. Vanessa Alcalá-Rmz & Laura A. Zanella-Calzada & Carlos E. Galván-Tejada & Alejandra García-Hernández & Miguel Cruz & Adan Valladares-Salgado & Jorge I. Galván-Tejada & Hamurabi Gamboa-Rosales, 2019. "Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks," IJERPH, MDPI, vol. 16(3), pages 1-12, January.
    7. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    8. Laura Zoboroski & Torrey Wagner & Brent Langhals, 2021. "Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use," IJERPH, MDPI, vol. 18(14), pages 1-15, July.
    9. Bowen Long & Fangya Tan & Mark Newman, 2023. "Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States," Forecasting, MDPI, vol. 5(1), pages 1-11, January.
    10. Alekh Gour & Shikha Aggarwal & Subodha Kumar, 2022. "Lending ears to unheard voices: An empirical analysis of user‐generated content on social media," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2457-2476, June.
    11. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    12. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    13. Beytía, Pablo & Infante, Carlos Cruz, 2020. "Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19," SocArXiv yndb7, Center for Open Science.
    14. Paulina Phoobane & Muthoni Masinde & Tafadzwanashe Mabhaudhi, 2022. "Predicting Infectious Diseases: A Bibliometric Review on Africa," IJERPH, MDPI, vol. 19(3), pages 1-20, February.
    15. Daniel Alejandro Gónzalez-Bandala & Juan Carlos Cuevas-Tello & Daniel E. Noyola & Andreu Comas-García & Christian A García-Sepúlveda, 2020. "Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    16. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    17. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    18. Songhee Cheon & Jungyoon Kim & Jihye Lim, 2019. "The Use of Deep Learning to Predict Stroke Patient Mortality," IJERPH, MDPI, vol. 16(11), pages 1-12, May.
    19. Rui Zhang & Zhen Guo & Yujie Meng & Songwang Wang & Shaoqiong Li & Ran Niu & Yu Wang & Qing Guo & Yonghong Li, 2021. "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China," IJERPH, MDPI, vol. 18(11), pages 1-14, June.
    20. Shruti Sharma & Yogesh Kumar Gupta & Abhinava K. Mishra, 2023. "Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods," IJERPH, MDPI, vol. 20(11), pages 1-23, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5959-:d:815382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.