Table 1.
Study characteristics
| 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 |
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