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Table 1 Study characteristics

From: Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Author

Year

Country

Population

Outcome

Methods used

Predictors

Sample size

EPV

Method of testing

Azeez et al. [25]

2014

Malaysia

ED

Triage level

NN, ANFIS

20

2223

 

Random split sample (70:30)

Caicedo-Torres et al. [26]

2016

Spain

ED

Discharge

LR, SVM, NN

147

1205

 

Random split sample (80:20), 10-fCV

Cameron et al. [27]

2015

Scotland

ED

Hospitalisation

LR

9

215231

 

Random split sample (66:33), bootstrapping (10,000)

Dinh et al. [28]

2016

Australia

ED

Hospitalisation

LR

10

860832

9470

Random split sample (50:50)

Dugas et al. [29]

2016

USA

ED

Critical illness

LR

9

97000000

525

Random split sample (90:10), 10f-CV

Golmohammadi [30]

2016

USA

ED

Hospitalisation

LR, NN

8

7266

460.25

Split sample (70:30)

Goto et al. [31]

2019

USA

ED

Critical illness, hospitalisation

LR, LASSO, RF, GBDT, DNN

5

52037

32.60

Random split sample (70:30)

Hong et al. [32]

2018

USA

ED

Hospitalisation

LR, GBDT, DNN

972

560486

171.44

Random split sample (90:10)

Kim, D et al. [33]

2018

Korea

Prehospital

Critical illness

LR, RF, DNN

5

460865

3583.60

10f-CV

Kim, S et al. [34]

2014

Australia

ED

Hospitalisation

LR

8

100123

1074.86

Apparent performance

Kwon et al. (1) [35]

2018

Korea

ED

Critical illness, hospitalisation

DNN, RF

7

10967518

133667.89

Split sample (50:50), + external validation dataset

Kwon et al. (2) [36]

2019

Korea

ED

Critical illness, hospitalisation

DNN, RF, LR

8

2937078

14047.57

Split sample (50:50)

Levin et al. [37]

2018

USA

ED

Critical illness, hospitalisation

RF

6

172726

56.74

Random split sample (66:33), bootstrapping

Li et al. [38]

2009

USA

Pre-hospital

Hospitalisation

LR, NB, DT, SVM

6

2784

 

10f-CV

Meisel et al. [39]

2008

USA

Pre-hospital

Hospitalisation

LR

9

401

 

Bootstrap resampling (1000)

Newgard et al. [40]

2013

USA

Prehospital

Critical illness

CART

40

89261

 

Cross-validation

Olivia et al. [41]

2018

India

ED

Triage level

DT, SVM, NN, NB

8

  

10f-CV

Raita et al. [42]

2019

USA

ED

Critical illness, hospitalisation

LR, LASSO, RF, GBDT, DNN

6

135470

107

Random split sample (70:30)

Rendell et al. [43]

2019

Australia

ED

Hospitalisation

B, DT, LR, NN, NB, KNN

11

1721294

5521

10f-CV

Seymour et al. [44]

2010

USA

Prehospital

Critical illness

LR

12

144913

156

Random split sample (60:40)

van Rein et al. [45]

2019

Netherlands

Prehospital

Critical illness

LR

48

6859

3.4375

Separate external validation

Wang et al. [46]

2013

Taiwan

ED

Triage level

SVM

6

3000

 

10f-CV

Zhang et al. [47]

2017

USA

ED

Hospitalisation

LR, NN

25

47200

91.8

10f-CV

Zlotnik et al. [48]

2016

Spain

ED

Hospitalisation

NN

9

153970

614.5

10f-CV

Zmiri et al. [49]

2012

Israel

ED

Triage level

NB, C4.5

4

402

 

10f-CV

  1. ANFIS Adaptive Neuro-Fuzzy Inference System, B Bayesian Network, CART Classification and Regression Tree, DT Decision Tree, DNN Deep Neural Network, EPV Events Per Variable, GBDT Gradient Boosted Decision Tree, KNN K-Nearest Neighbours, LR logistic regression, LASSO Least Absolute Shrinkage and Selection Operator, NB Naïve Bayes, NN Neural Network, RF Random Forest, SVM Support Vector Machine