Predictive Models for Emergency Department Triage using Machine Learning: A Review

Article Information

Fei Gao1*, Baptiste Boukebous3, 4, Pozzar Mario1, 2, Alaoui Enora1, 2, Sano Batourou1, 2, Sahar Bayat-Makoei1

1University of Rennes, EHESP, CNRS, Inserm, Research on Health Services and Management, Rennes, France

2University of Rennes, Rennes, France

3ECAMO, UMR1153, Centre of Research in Epidemiology and StatisticS, INSERM, Paris, France

4Hoptial Bichat /Beaujon, APHP, Paris, France

*Corresponding Author: Fei Gao, EHESP School of Public Health, Department of Quantitative Methods for Public Health, Avenue of Professor Léon Bernard, 35043 Rennes, France

Received: 15 March 2022; Accepted: 25 March 2022; Published: 05 April 2022


Fei Gao, Baptiste Boukebous, Pozzar Mario, Alaoui Enora, Sano Batourou, Sahar Bayat-Makoei. Predictive Models for Emergency Department Triage using Machine Learning: A Review. Obstetrics and Gynecology Research 5 (2022): 107-121.

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Background: Recently, many research groups have tried to develop emergency department triage decision support systems based on big volumes of historical clinical data to differentiate and prioritize patients. Machine learning models might improve the predictive capacity of emergency department triage systems. The aim of this review was to assess the performance of recently described machine learning models for patient triage in emergency departments, and to identify future challenges.

Methods: Four databases (ScienceDirect, PubMed, Google Scholar and Springer) were searched using key words identified in the research questions. To focus on the latest studies on the subject, the most cited papers between 2018 and October 2021 were selected. Only works with hospital admission and critical illness as outcomes were included in the analysis.

Results: Eleven articles concerned the two outcomes (hospital admission and critical illness) and developed 55 predictive models. Random Forest and Logistic Regression were the most commonly used prediction algorithms, and the receiver operating characteristic-area under the curve (ROC-AUC) the most frequently used metric to assess the algorithm prediction performance. Random Forest and Logistic Regression were the most discriminant models according to the selected studies.

Conclusions: Machine learning-based triage systems could improve decision-making in emergency departments, thus leading to better patients’ outcomes. However, there is still scope for improvement concerning the prediction performance and explicability of ML models.


Triage, Emergency Department/Emergency Room, Machine Learning, Modeling,Model, Classification,Predictive, Artificially Intelligence, Decision Support Systems, Patient Prioritization

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Article Details

1. Background

Emergency departments (ED), where diagnostic and therapeutic interventions must be executed rapidly and effectively [1], are one of the biggest sources of hospitalization [2, 3]. On arrival at the ED, patients are first classified according to the severity of their condition, in order to prioritize those requiring immediate medical intervention. This triage is usually performed by a nurse on the basis of the patients’ vital signs and main complaint [4-5]. Recently, there has been increased interest in developing ED triage decision support systems based on big volumes of historical clinical data to differentiate and prioritize patients. Several studies showed that machine learning (ML) prediction models are valuable for improving ED triage of patients [3, 6-10]. The aim of this review was to assess the performance of recently described ML models used for patient triage in ED, and to identify the future challenges.

2. Method

2.1 Study search and eligibility criteria

Four databases (ScienceDirect, PubMed, Google Scholar and Springer) were manually searched using key words (“triage”, “emergency department”/ “emergency room”, “machine learning”, “modeling”, “model”, “classification” ,“predictive”, “artificially intelligence”, “decision support systems”, “patient prioritization”) identified in the research questions , as done in previous studies [11-16]. Studies were selected in two steps. First, studies published between 2018 and October 2021, with the highest number of citations, and with hospital admission and/or critical illness as outcomes were pre-selected. Then, the final selection was based on sample size, number and type of feature variables, type of model (s) constructed, and programming language/statistical tools.

3. Results

3.1 Study selection

First, 19 papers published between 2018 and October 2021 were pre-selected. Their characteristics are summarized in Table 1. Then, 11 studies were analy-zed in detail (final selection: highlighted in Table 1): six studies performed in the USA (one included data from USA and Portugal), two in Korea, one in the Netherlands, one in Northern Ireland, and one in Australia.

3.2 Data sample and predictors

In all selected articles, the study population conce-rned patients visiting the ED, with the exception of the article by Kim et al. in which the study focused on the prehospital environment [17]. Sample sizes varied from ~20,000 to ~3,000,000 individuals. Figure 1 summarizes the variables used to build the ML models in each study. Hong et al. [18] included 972 explanatory variables, while the other articles used fewer than 20 predictors. Although the data used for the ML model implementation were specific to each study, several common categories could be identified, such as demographic variables (age and sex), clinical variables (vital signs and diagnosis), arrival information (time and transport mode), ED visit outcome (hospital admission or discharge). Hong et al., Raita et al. and Araz et al. took into account also the Emergency Severity Index [19, 18, 20]. Seven articles linked data to the common main complaints, but only Goto et al. [21] included information on comorbidities. Less than half of the articles presented information on the use of hospital metrics (e.g. number of previous ED visits and number of previous hospitalizations). Only Hong et al. [18], Rendell et al. [22] and Levin et al. [23] included the patients’ past medical history. Hong et al. [18] and De Hond et al. [24] added also information on historical laboratory test results, and imaging and electrocardiogram exams. For each article, the included variables are shown by a green diamond. Variables that were not included (or not available) and variables for which no clear information was found are shown with red and gray diamonds, respectively.

3.3 Machine learning process

3.3.1 Candidate variable handling and feature engineering: In the majority of the selected studies, all variables were included in the implemented models (Figure 2). Rendell et al. [22], Kwon et al. [25], Fernandes et al. [26] and Araz et al. [19] used Stepwise or Correlation-based methods for feature selection to reduce the number of input variables. When building a predictive model, it is often possible to improve its predictive performance by trans-forming variables. The most common transformation methods include categorization (e.g. bucketing, binning), interactions, and polynomial or spline transformation for numerical variables. Only Rendell et al. proposed predictor interactions features [22]. None of the authors used polynomial or spline transformation. Levin et al. [27] and Kim et al. [17] did not provide any clear information on the variables retained in their models.

3.3.2 Data resampling: In most articles, the datasets were randomly partitioned into training and test datasets (Table 1). The percentage of data contained in each dataset differed among studies (e.g. 90:10 in the study by Hong et al. [18], and 70:30 in the study by Raita et al. [20]). Levin et al. used the boot-strapping resampling technique [23]. Nine studies used the cross-validation method to validate the model performance or to tune hyperparameters, which helps to avoid the risk of overfitting or underfitting [28, 29].

3.3.3 Prediction algorithms and calibration of hyperparameters: In total, 55 models were used to predict hospital admission or critical illness outcomes (Figure 3). Random Forest and Logistic Regression were the two most widely used models (n=9/11 articles), followed by Gradient Boosting and Deep Neural Network models (n=6/11 studies). Conv-ersely, some models were only used in one study: K-Nearest Neighbors and Naive Bayes (Rendell et al. [30]), Support Vector Machine (Araz and al. [19]), and Random Under Sampling Boost (Fernandes et al. [26]). Among the used tools, R and Python were the most common, followed by Matlab and the SQL language. With the exception of the articles by Rendell et al. [30] and Levin et al. [27], in all other studies at least one hyperparameter was calibrated, depending on the method used.

3.3.4 Evaluation metrics: The metrics used to evaluate the performance of the different models (Figure 4) included the F1 score, the receiver operating characteristic-area under the curve (ROC-AUC), sensitivity and specificity, and accuracy. The sensitivity and specificity and ROC-AUC metrics were the most used.

3.3.5 Model agnostic methods: Most authors used Logistic Regression coefficients to identify signify-cant variables. For models that cannot be interpreted directly, such as Random Forests, Gradient Boosting and Neural Networks, the Permutation Feature Importance model-agnostic method was used in seven studies to identify the variables that most contributed to discrimination [17, 18, 20, 21, 24, 26, 27]. This method assesses the predictor importance by measuring the increase of the prediction error when the feature values are permuted.

3.4 Model performance assessment

3.4.1 Hospitalization outcome: In the selected studies, 44 models were developed (Table 1) with hospital admission as outcome. Figure 5 illustrates the performance of the prediction models based on the C-statistic method (AUC). Gradient Boosting was the most discriminant (median AUC = 0.860 and interquartile ranges (IQR) = 0.859-0.863), compared with Logistic Regression (median AUC = 0.840, IQR = 0.815-0.850) and Single Layer Neural Networks (median AUC = 0.825, IQR = 0.820-0.830), and also Deep Neural Networks and K-Nearest Neighbors (median AUC = 0.82 for both, IQR = 0.800-0.860 and 0.815-0.850, respectively).

3.4.2 Critical illness: Eleven models used critical illness as outcome measure (Figure 6). Deep Neural Networks displayed the best performance in differentiating between patients with and without a critical illness (median AUC = 0.875, IQR = 0.857-0.895), followed by Random Forest (median AUC = 0.870, IQR = 0.850-0.881), Logistic Regression (median AUC = 0.851, IQR = 0.846-0.860), and GradientBoosting (median AUC = 0.840, only one model). Figure 7 shows the most relevant variables according to the Permutation Feature Importance model-agnostic method: age, sex, mode of transport to the ED, vital signs, and common chief complaints. For each article, the significant variables are shown by green diamonds. Not relevant variables and variables for which no clear information on their relevance was given in the selected articles are indicated by red and gray circles with a cross, respectively.


Figure 1: Predictors/candidate variables included in the selected articles.


Figure 2: Candidate variable handling and feature engineering for model building in the different studies. Green diamond, yes; red circle with a cross, no; gray circle with a cross, no clear information.


Figure 3: Algorithms used in the selected studies. Green diamond, algorithm used in that study; red circle with a cross, not used in that study.


Figure 4: Evaluation metrics used in the included studies.


Figure 5: C-statistic of the algorithms used to predict hospitalization.


Figure 6: C-statistic of the algorithms used to predict critical illness.


Figure 7: Relevant variables.

Table icon

Table 1: Characteristics of the selected articles.

4. Discussion

The objective of these studies that developed ED triage algorithms was to propose decision support systems to help health professionals in prioritizing high-risk patients. As mentioned in previous review articles [17-21, 25-27, 31], the reference standard on which ED triage is currently based, such as the Emergency Severity Index, can hardly recognize critically ill patients. Indeed, it is hard to deal with such detailed data on the little time available. Advanced artificial intelligence (AI) models based on big volumes of historical clinical data may allow overcoming this obstacle.

The aim of the present review was to identify the tools needed to build robust and efficient prediction algorithms that offer higher discrimination perfor-mance than the reference standard models. The eleven recent and most cited studies from 2018 to October 2021, selected for this review, described ML-based decision support systems to improve patient triage in ED. Two outcomes were selected: hospital admission and critical illness. The most common methods were Random Forest and Logistic Regression (12 models/each), followed by Gradient Boosting (11 models) and Deep Neural Networks (10 models).

The objective of this review was not only to describe the developed methods and techniques, but also to identify possible improvements. A common problem with the selected studies was that they did not describe in detail or did not report their feature engineering process. Only one study mentioned that they took into account the predictor interactions [30]. No study explained how they would model non-linear numerical predictors and non-linear relationships (e.g. polynomials or splines). Furthermore, nine of the included studies mentioned that they took into account the hyperparameter calibration [18-21, 24, 26, 31]. However, the majority did not explain the rationale behind the choice of calibration method and did not include the results of this analysis. Yet, the calibration result analysis might be crucial during the development of a transportable model that needs to be adapted to new settings [10, 38-40].

Many authors mentioned the necessity to offer the widest possible range of prediction approaches. For example, Rendell et al. highlighted the different advantages of each ML algorithm and emphasized that these algorithms overcome the limitations of more traditional regression techniques by offering both linear and non-linear decision forms. However, in our selected studies, only two studies implemented six models [19, 22], and most proposed only three to four prediction algorithms. Lastly, model-agnostic interpretation methods help to understand how features can affect the model prediction. They are flexible and can be applied to any ML model to find new patterns and to know more about the dataset [41-43]. In the selected studies, the authors used exclusively the Permutation Feature Importance method to identify relevant features. Other methods, such as Partial Dependence Plot, Accumulated Local Effect Plots, Feature interaction (H-statistic), Functional Decomposition, and Global Surrogate Models, could be investigated in future works to identify predictors that might affect the patient triage prediction [41].

5. Conclusion

This review found that combining machine learning with historical clinical data for patient triage in ED has a clear advantage over the reference standard currently in use. However, there is still scope for improvement to enhance the prediction performance and explicability of ML models: 1) integration of predictors’ interactions and non-linear relationships; 2) precise information on hyperparameter calibration to make models more transportable, and 3) more studies on the different model-agnostic interpretation methods to identify predictors that affect the triage process. The goal is to optimize the patient flow in order to improve their management, reduce waiting time, and efficiently use resources [44, 45].


Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

All data generated or analyzed during this study are included in this published article. If readers need supplementary information, they can contact me (

Competing interests

The authors declare that they have no competing interests.


Not applicable.

Authors’ contributions

FG designed the project, performed the statistical analysis and drafted the manuscript. SD supervised the overall project, oversaw the statistical analysis, and helped to draft and revised the manuscript. CL, KK and MG performed the statistical analysis with FG and SD. BB gave important suggestions to this study. All authors interpreted the data and reviewed the manuscript for important intellectual content. All authors have read and approved the final version of the manuscript.


This research is supported by EHESP Rennes, Univ Rennes, EHESP, CNRS, Inserm, Arènes - UMR 6051, RSMS - U 1309, ECAMO, Hoptial Bichât - APHP and ENSAI. Points of view or opinions in this article are those of the authors and do not necessarily represent the official position or policies of the EHESP Rennes, UMR 6051, RSMS - U 1309, ECAMO, Hoptial Bichât - APHP and ENSAI.


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