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Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

Author

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  • Victor Olsavszky

    (Department of Dermatology, Venereology and Allergy, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Theodor-Kutzer-Ufer 1–3, 68167 Mannheim, Germany)

  • Mihnea Dosius

    (National School of Public Health Management and Professional Development, Str. Vaselor, nr. 31, 030167 Bucharest, Romania)

  • Cristian Vladescu

    (National School of Public Health Management and Professional Development, Str. Vaselor, nr. 31, 030167 Bucharest, Romania
    University of Medicine and Pharmacy Victor Babes, Piaţa Eftimie Murgu, nr.2, 300041 Timisoara, Romania)

  • Johannes Benecke

    (Department of Dermatology, Venereology and Allergy, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Theodor-Kutzer-Ufer 1–3, 68167 Mannheim, Germany)

Abstract

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:4979-:d:382934
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    References listed on IDEAS

    as
    1. Svitlana Volkova & Ellyn Ayton & Katherine Porterfield & Courtney D Corley, 2017. "Forecasting influenza-like illness dynamics for military populations using neural networks and social media," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-22, December.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    4. Chris Allen & Ming-Hsiang Tsou & Anoshe Aslam & Anna Nagel & Jean-Mark Gawron, 2016. "Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-10, July.
    5. Yuanbin Song & Fan Wang & Bin Wang & Shaohua Tao & Huiping Zhang & Sai Liu & Oscar Ramirez & Qiyi Zeng, 2015. "Time Series Analyses of Hand, Foot and Mouth Disease Integrating Weather Variables," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-18, March.
    6. 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.
    7. 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.
    8. Fan, Victoria Y & Jamison, Dean T & Summers, Lawrence H, 2018. "Pandemic risk: how large are the expected losses?," Scholarly Articles 35014363, Harvard Kennedy School of Government.
    9. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    10. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    11. Shinya Suzuki & Takeshi Yamashita & Tsuyoshi Sakama & Takuto Arita & Naoharu Yagi & Takayuki Otsuka & Hiroaki Semba & Hiroto Kano & Shunsuke Matsuno & Yuko Kato & Tokuhisa Uejima & Yuji Oikawa & Minor, 2019. "Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.
    12. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2018. "Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-29, June.
    13. Mihaela Simionescu & Svitlana Bilan & Beata Gavurova & Elena-Nicoleta Bordea, 2019. "Health Policies in Romania to Reduce the Mortality Caused by Cardiovascular Diseases," IJERPH, MDPI, vol. 16(17), pages 1-9, August.
    14. Ahmed M Alaa & Thomas Bolton & Emanuele Di Angelantonio & James H F Rudd & Mihaela van der Schaar, 2019. "Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-17, May.
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    Cited by:

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    2. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.

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