This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Artificial Intelligence (1371) Outbreak and Pandemic Preparedness and Management (1728) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Infectious Diseases (non-STD/STI) (1575) Machine Learning (1465) Innovative Methods in Public Health and Surveillance (332) Surveillance Systems (462)

Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27806, first published .
A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

Authors of this article:

Cheng-Sheng Yu1, 2, 3, 4 Author Orcid Image ;   Shy-Shin Chang1, 2 Author Orcid Image ;   Tzu-Hao Chang3, 5 Author Orcid Image ;   Jenny L Wu1, 3 Author Orcid Image ;   Yu-Jiun Lin1, 2 Author Orcid Image ;   Hsiung-Fei Chien6 Author Orcid Image ;   Ray-Jade Chen4, 7, 8 Author Orcid Image
Article Authors Cited by (27) Tweetations (10) Metrics

Journals

  1. Ku S, Liu H, Su C, Yeh I, Yen M, Anuraga G, Ta H, Chiao C, Xuan D, Prayugo F, Wang W, Wang C. Comprehensive analysis of prognostic significance of cadherin (CDH) gene family in breast cancer. Aging 2022:8498 View
  2. Shaibani M, Emamgholipour S, Moazeni S. Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran. Stochastic Environmental Research and Risk Assessment 2022;36(9):2461 View
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  9. Xuan D, Yeh I, Wu C, Su C, Liu H, Chiao C, Ku S, Jiang J, Sun Z, Ta H, Anuraga G, Wang C, Yen M, Xu B. Comparison of Transcriptomic Signatures between Monkeypox-Infected Monkey and Human Cell Lines. Journal of Immunology Research 2022;2022:1 View
  10. Zheng H, An S, Qiao B, Guan P, Huang D, Wu W. A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA. Environmental Science and Pollution Research 2022;30(5):13648 View
  11. Alali Y, Harrou F, Sun Y. A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Scientific Reports 2022;12(1) View
  12. ÇALIŞKAN A. DALGACIK EVRİŞİMSEL SİNİR AĞI YÖNTEMİ İLE KORONAVİRÜS HASTALIĞININ TESPİTİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 2023;26(1):203 View
  13. Kiganda C, Akcayol M. Forecasting the Spread of COVID-19 Using Deep Learning and Big Data Analytics Methods. SN Computer Science 2023;4(4) View
  14. Pirmani A, De Brouwer E, Geys L, Parciak T, Moreau Y, Peeters L. The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research. JMIR Medical Informatics 2023;11:e48030 View
  15. Talaat M, Si X, Xi J. Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images. Applied Sciences 2023;13(18):10270 View
  16. Chang T, Chen Y, Lu H, Wu J, Mak K, Yu C. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine 2024;103(7):e37112 View
  17. Kim D, Cánovas-Segura B, Campos M, Juarez J. Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review. Technologies 2024;12(3):31 View
  18. Winalai C, Anupong S, Modchang C, Chadsuthi S. LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach. Heliyon 2024;10(9):e30319 View
  19. Cumbane S, Gidófalvi G. Deep learning-based approach for COVID-19 spread prediction. International Journal of Data Science and Analytics 2024 View
  20. Demongeot J, Magal P, Oshinubi K. Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19. Mathematical Medicine and Biology: A Journal of the IMA 2024 View

Books/Policy Documents

  1. Guest P, Popovic D, Steiner J. Multiplex Biomarker Techniques. View
  2. Vanitha V, Kumaran P. System Design for Epidemics Using Machine Learning and Deep Learning. View
  3. Guest P, Hawkins S, Rahmoune H. Application of Omic Techniques to Identify New Biomarkers and Drug Targets for COVID-19. View