Published on in Vol 21, No 2 (2019): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12341, first published .
An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

Journals

  1. Scarpino S, Scott J, Eggo R, Clements B, Dimitrov N, Meyers L, Ferrari M. Socioeconomic bias in influenza surveillance. PLOS Computational Biology 2020;16(7):e1007941 View
  2. Shull J. Digital Health and the State of Interoperable Electronic Health Records. JMIR Medical Informatics 2019;7(4):e12712 View
  3. Lo Y, Yang C, Chien H, Chang S, Lu C, Chen R. Blockchain-Enabled iWellChain Framework Integration With the National Medical Referral System: Development and Usability Study. Journal of Medical Internet Research 2019;21(12):e13563 View
  4. Choi H, Choi W, Han E. Suggestion of a simpler and faster influenza-like illness surveillance system using 2014–2018 claims data in Korea. Scientific Reports 2021;11(1) View
  5. Jahja M, Chin A, Tibshirani R. Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion. Statistical Science 2022;37(2) View
  6. Wang M, Lee C, Wang W, Yang Y, Yang C, Jiang Y. Early Warning of Infectious Diseases in Hospitals Based on Multi-Self-Regression Deep Neural Network. Journal of Healthcare Engineering 2022;2022:1 View
  7. Hammond A, Kim J, Sadler H, Vandemaele K. Influenza surveillance systems using traditional and alternative sources of data: A scoping review. Influenza and Other Respiratory Viruses 2022;16(6):965 View
  8. Rosenfeld R, Tibshirani R. Epidemic tracking and forecasting: Lessons learned from a tumultuous year. Proceedings of the National Academy of Sciences 2021;118(51) View
  9. Chang T, Liu Y, Lin S, Chiu P, Chou C, Chang L, Lai F. Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission. Journal of Microbiology, Immunology and Infection 2023;56(4):772 View
  10. Zhang L, Li M, Zhi C, Zhu M, Ma H. Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention. Current Medical Science 2024 View