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

Machine Learning (1971) Secondary Use of Clinical Data for Research and Surveillance (453) Natural Language Processing (869)

Published on in Vol 10, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34504, first published .
Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation

Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation

Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation

Authors of this article:

Bi Fan1 Author Orcid Image ;   Jiaxuan Peng2 Author Orcid Image ;   Hainan Guo1 Author Orcid Image ;   Haobin Gu3 Author Orcid Image ;   Kangkang Xu4 Author Orcid Image ;   Tingting Wu1 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

Journals

  1. Tuominen J, Koivistoinen T, Kanniainen J, Oksala N, Palomäki A, Roine A. Early Warning Software for Emergency Department Crowding. Journal of Medical Systems 2023;47(1) View
  2. Stewart J, Innes M, Goudie A. The potential impact of artificial intelligence on emergency department overcrowding and access block. Emergency Medicine Australasia 2024;36(4):632 View
  3. McClymont H, Lambert S, Barr I, Vardoulakis S, Bambrick H, Hu W. Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic. Journal of Epidemiology and Global Health 2024;14(3):645 View
  4. Förstel M, Haas O, Förstel S, Maier A, Rothgang E. A Systematic Review of Features Forecasting Patient Arrival Numbers. CIN: Computers, Informatics, Nursing 2025;43(1) View
  5. Parrales-Bravo F, Caicedo-Quiroz R, Tolozano-Benites E, Vasquez-Cevallos L, Cevallos-Torres L. STOP: Studying Time-Series of Preeclamptic Emergencies. IEEE Access 2025;13:65672 View
  6. Ming C, Lee G, Teo Y, Teo Y, Zhou X, Ho E, Toh E, Ong M, Tan B, Ho A. Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price. Computer Methods and Programs in Biomedicine 2025;267:108808 View
  7. Agius S, Cassar V, Bezzina F, Topham L. Leveraging digital technologies to enhance patient safety. Health and Technology 2025 View
  8. Petravić L, Gril Rogina K, Albreht T, Kukec A, Žibert J. Prognostic models for predicting patient arrivals in emergency departments: an updated systematic review and research agenda. BMC Emergency Medicine 2025;25(1) View

Conference Proceedings

  1. Békésy M. 2023 IEEE 21st Jubilee International Symposium on Intelligent Systems and Informatics (SISY). Forecasting Patient Arrival Trends to the Emergency Department Based on Weather: A Scoping Review View
  2. Parrales–Bravo F, Caicedo–Quiroz R, Barzola–Monteses J, Cevallos–Torres L. 2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB). Prediction of Emergency Room Arrivals of Patients with Preeclampsia Disease Using Artificial Neural Network Model View