Machine learning model identification and prediction of patients' need for ICU admission: A systematic review

Yujing Chen; Han Chen; Qian Sun; Rui Zhai; Xiaowei Liu; Jianyi Zhou; Shufang Li

doi:10.1016/j.ajem.2023.08.043

Machine learning model identification and prediction of patients' need for ICU admission: A systematic review

Am J Emerg Med. 2023 Nov:73:166-170. doi: 10.1016/j.ajem.2023.08.043. Epub 2023 Sep 4.

Authors

Yujing Chen¹, Han Chen², Qian Sun¹, Rui Zhai¹, Xiaowei Liu¹, Jianyi Zhou¹, Shufang Li³

Affiliations

¹ The Eighth Clinical Medical College, Guangzhou University of Chinese Medicine, Foshan, Guangdong, China.
² Health Science Center, Yangtze University, Jingzhou, Hubei, China.
³ The Eighth Clinical Medical College, Guangzhou University of Chinese Medicine, Foshan, Guangdong, China. Electronic address: lcfightiang@qq.com.

PMID: 37696074
DOI: 10.1016/j.ajem.2023.08.043

Abstract

Background: The emergency department (ED) triage process serves as a crucial first step for patients seeking acute care, This initial assessment holds crucial implications for patient survival and prognosis. In this study, a systematic review of the existing literature was performed to investigate the performance of machine learning (ML) models in recognizing and predicting the need for intensive care among ED patients.

Methods: Four prominent databases (PubMed, Embase, Cochrane Library and Web of Science) were searched for relevant literature published up to April 28, 2023. The Prediction model study Risk of Bias Assessment Tool (PROBAST) was employed to evaluate the risk of bias and feasibility of prediction models.

Results: In ten studies, the main algorithms used were Gradient Boostin, Logistic Regressio, Neural Network, Support Vector Machines, Random Forest. The performance of each model was as follows: Gradient Boosting had a sensitivity range of 0.3 to 0.96, specificity range of 0.6 to 0.99, accuracy range of 0.37 to 0.99, precision range of 0.3 to 0.96, and AUC value range of 0.68 to 0.93; Logistic Regression had a sensitivity range of 0.46 to 0.97, specificity range of 0.28 to 0.99, accuracy range of 0.66 to 0.97, precision range of 0.27 to 0.63, and AUC value range of 0.72 to 0.97; Neural Networks had a sensitivity range of 0.45 to 0.96, specificity range of 0.58 to 0.99, accuracy range of 0.36 to 0.97, precision range of 0.27 to 0.96, and AUC value range of 0.67 to 0.91; Support Vector Machines had a sensitivity range of 0.49 to 0.83, specificity range of 0.94 to 0.98, accuracy range of 0.33 to 0.97, precision range of 0.53 to 0.94, and AUC values were not reported; Random Forests had a sensitivity range of 0.75 to 0.91, specificity range of 0.77 to 0.94, accuracy range of 0.35 to 0.77, precision range of 0.36 to 0.94, and AUC value of 0.83.

Conclusion: ML models have demonstrated good performance in identifying and predicting critically ill patients in ED triage. However, because of the limited number of studies on each model, further high-quality prospective research is needed to validate these findings.

Keywords: Emergency department; Intensive care; Machine learning; Triage.

Publication types

Review