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

Machine Learning (1501) Clinical Informatics (1050) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210)

Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13075, first published .
Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

Authors of this article:

Junfeng Peng1 Author Orcid Image ;   Chuan Chen1 Author Orcid Image ;   Mi Zhou2 Author Orcid Image ;   Xiaohua Xie1 Author Orcid Image ;   Yuqi Zhou3 Author Orcid Image ;   Ching-Hsing Luo1 Author Orcid Image
Article Authors Cited by (11) Tweetations (1) Metrics

Journals

  1. Wei Y, Li W, Tan J, Yuan J, Wu Z, Li Y, Mao Y, Huang D, Dai Q. A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis. Computational and Mathematical Methods in Medicine 2022;2022:1 View
  2. Ravindra K, Bahadur S, Katoch V, Bhardwaj S, Kaur-Sidhu M, Gupta M, Mor S. Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections. Science of The Total Environment 2023;858:159509 View
  3. Neo E, Hasikin K, Mokhtar M, Lai K, Azizan M, Razak S, Hizaddin H. Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review. Frontiers in Public Health 2022;10 View
  4. Seo S, Min C, Preston M, Han S, Choi S, Kang S, Kim D. Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring. Sustainability 2022;14(5):2703 View
  5. Neo E, Hasikin K, Lai K, Mokhtar M, Azizan M, Hizaddin H, Razak S, Yanto . Artificial intelligence-assisted air quality monitoring for smart city management. PeerJ Computer Science 2023;9:e1306 View
  6. Meyer L, Stead S, Salge T, Antons D. Artificial intelligence in acute care: A systematic review, conceptual synthesis, and research agenda. Technological Forecasting and Social Change 2024;206:123568 View
  7. Chou-Chen S, Barboza L. Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data. Mathematical Biosciences and Engineering 2024;21(7):6539 View
  8. Cappelli F, Castronuovo G, Grimaldi S, Telesca V. Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases. International Journal of Environmental Research and Public Health 2024;21(7):867 View

Books/Policy Documents

  1. Kasten J. Research Anthology on Big Data Analytics, Architectures, and Applications. View
  2. Saini J, Dutta M, Marques G. Internet of Things: Architectures for Enhanced Living Environments. View