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

Theme Issue: Medical Informatics and COVID-19 (107) JMIR Theme Issue: COVID-19 Special Issue (2452) Clinical Information and Decision Making (1407) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Outbreak and Pandemic Preparedness and Management (1727) Infectious Diseases (non-STD/STI) (1575) Decision Support for Health Professionals (1185) Artificial Intelligence (1371) Machine Learning (1465)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32726, first published .
Optimal Triage for COVID-19 Patients Under Limited Health Care Resources With a Parsimonious Machine Learning Prediction Model and Threshold Optimization Using Discrete-Event Simulation: Development Study

Optimal Triage for COVID-19 Patients Under Limited Health Care Resources With a Parsimonious Machine Learning Prediction Model and Threshold Optimization Using Discrete-Event Simulation: Development Study

Optimal Triage for COVID-19 Patients Under Limited Health Care Resources With a Parsimonious Machine Learning Prediction Model and Threshold Optimization Using Discrete-Event Simulation: Development Study

Authors of this article:

Jeongmin Kim1 Author Orcid Image ;   Hakyung Lim1 Author Orcid Image ;   Jae-Hyeon Ahn1 Author Orcid Image ;   Kyoung Hwa Lee2 Author Orcid Image ;   Kwang Suk Lee3 Author Orcid Image ;   Kyo Chul Koo3 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

Journals

  1. Duggal A, Mathews K. Impact of ICU strain on outcomes. Current Opinion in Critical Care 2022;28(6):667 View
  2. Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. Journal of Business Research 2023;160:113806 View
  3. Atalan A, Şahin H, Atalan Y. Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources. Healthcare 2022;10(10):1920 View
  4. Kim G. Multi-layer Stacking Ensemble-based Clinical Severity Prediction Model for Early Triage of COVID-19 Patients. The Journal of Korean Institute of Information Technology 2022;20(12):37 View
  5. Lim J. Diagnostic Accuracy of Machine Learning Algorithms for Hepatitis A Antibody. The Korean Journal of Aerospace and Environmental Medicine 2022;32(1):16 View
  6. Baek S, Jeong Y, Kim Y, Kim J, Kim J, Kim E, Lim J, Kim J, Kim Z, Kim K, Chung M. Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study. Journal of Medical Internet Research 2024;26:e52134 View
  7. Wang Y, Zhang W, Lu Z, Sun J, Jing M. Optimal resource allocation model for COVID-19: a systematic review and meta-analysis. BMC Infectious Diseases 2024;24(1) View
  8. Kim G, Ju C, Seok H, Lee D. Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients. Applied Sciences 2024;14(7):2715 View
  9. Meerwijk E, McElfresh D, Martins S, Tamang S. Evaluating accuracy and fairness of clinical decision support algorithms when health care resources are limited. Journal of Biomedical Informatics 2024;156:104664 View
  10. Herington J, Shand J, Holden-Wiltse J, Corbett A, Dees R, Ching C, Shaw M, Cai X, Zand M, Go R. Investigating ethical tradeoffs in crisis standards of care through simulation of ventilator allocation protocols. PLOS ONE 2024;19(9):e0300951 View