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

Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Artificial Intelligence (1421) Machine Learning (1501)

Published on in Vol 9, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30770, first published .
Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis

Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis

Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis

Authors of this article:

Hyoungju Yun1 Author Orcid Image ;   Jinwook Choi1, 2, 3 Author Orcid Image ;   Jeong Ho Park4, 5 Author Orcid Image
Article Authors Cited by (14) Tweetations (4) Metrics

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  1. Yu J, Xie F, Nan L, Yoon S, Ong M, Ng Y, Cha W. An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department. Scientific Reports 2022;12(1) View
  2. Chang H, Cha W. Artificial intelligence decision points in an emergency department. Clinical and Experimental Emergency Medicine 2022;9(3):165 View
  3. Tsai D, Tsai S, Chiang H, Lee C, Chen S. Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department. Journal of Personalized Medicine 2022;12(5):700 View
  4. Chang H, Yu J, Yoon S, Kim T, Cha W. Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage. Scientific Reports 2022;12(1) View
  5. Pettit R, Fullem R, Cheng C, Amos C. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerging Topics in Life Sciences 2021;5(6):729 View
  6. Khan S, Perkins A, Fuchita M, Holler E, Ortiz D, Boustani M, Khan B, Gao S. Development of a population‐level prediction model for intensive care unit (ICU) survivorship and mortality in older adults: A population‐based cohort study. Health Science Reports 2023;6(10) View
  7. Tschoellitsch T, Seidl P, Böck C, Maletzky A, Moser P, Thumfart S, Giretzlehner M, Hochreiter S, Meier J. Using emergency department triage for machine learning-based admission and mortality prediction. European Journal of Emergency Medicine 2023;30(6):408 View
  8. Takkavatakarn K, Oh W, Cheng E, Nadkarni G, Chan L. Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrology 2023;24(1) View
  9. Wang P, Wu S, Tian M, Liu K, Cong J, Zhang W, Wei B. A conformal regressor for predicting negative conversion time of Omicron patients. Medical & Biological Engineering & Computing 2024 View
  10. Yu J, Kim D, Yoon S, Kim T, Heo S, Chang H, Han G, Jeong K, Park R, Gwon J, Xie F, Ong M, Ng Y, Joo H, Cha W. Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model. Scientific Reports 2024;14(1) View
  11. 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
  12. Chen X, Tang S, Qin Y, Zhou S, Zhang L, Huang Y, Chen Z. A Predictive Model of Pressure Injury in Children Undergoing Living Donor Liver Transplantation Based on Machine Learning Algorithm. Journal of Advanced Nursing 2024 View
  13. Porto B. Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review. BMC Emergency Medicine 2024;24(1) View
  14. Araouchi Z, Adda M. TriageIntelli: AI-Assisted Multimodal Triage System for Health Centers. Procedia Computer Science 2024;251:430 View