pISSN 2234-3806eISSN 2234-3814
| Authors (publication year) | Country | Patient group | Sample size, N | Key variables | Outcome | Best ML model | IHCA prediction performance, AUROC | Reference |
|---|---|---|---|---|---|---|---|---|
| Ong, et al. (2012) | Singapore | ED | 925 | Demographics, vital signs, HRV metrics | Cardiac arrest | SVM | 0.781 | [29] |
| Liu, et al. (2014) | Singapore | ED | 702 | Vital signs, HRV metrics | Major adverse cardiac events |
SVM | 0.812 | [30] |
| Churpek, et al. (2014) | US | GW | 269,999 | Demographics, vital signs, laboratory values | Cardiac arrest, ICU transfer, or death | RF (eCARTTM) | 0.77 | [31] |
| Green, et al. (2018) | US | GW | 107,868 | Demographics, vital signs, laboratory values | Cardiac arrest, ICU transfer, or death | RF (eCARTTM) | 0.801 | [32] |
| Bartkowiak, et al. (2018) | US | Postoperative | 32,537 | Demographics, vital signs, laboratory values | Cardiac arrest, ICU transfer, or death | RF (eCARTTM) | 0.79 | [33] |
| Kwon, et al. (2018) | South Korea | GW | 52,131 | Vital signs | Cardiac arrest or ICU transfer | LSTM(DeepCARSTM) | 0.850 | [34] |
| Jang, et al. (2019) | South Korea | ED | 374,605 | Demographics, chief complaint, vital signs, consciousness level | Cardiac arrest | MLP-LSTM | 0.936 | [35] |
| Kim, et al. (2019) | South Korea | ICU | 29,181 | Vital signs, treatment history, health status, recent surgery | Cardiac arrest | LSTM | 0.896 | [36] |
| Cho, et al. (2020) | South Korea | GW | 8,039 | Vital signs | Cardiac arrest or ICU transfer | LSTM(DeepCARSTM) | 0.865 | [37] |
| Chae, et al. (2021) | South Korea | GW | 83,543 | Demographics, vital signs, laboratory values | Cardiac arrest | Various | No data | [9] |
| Kim, et al. (2022) | South Korea | ED | 1,350,693 | Demographics, vital signs, oxygen supply, oxygen saturation, ED occupancy | Cardiac arrest | XGBoost | 0.927 | [11] |
| Lee, et al. (2023) | South Korea | ICU | 4,821 | HRV metrics | Cardiac arrest | LGBM | 0.881 | [13] |
| Cho, et al. (2023) | South Korea | GW | 55,083 | Vital signs | Cardiac arrest or ICU transfer | LSTM(DeepCARSTM) | 0.869 | [12] |
| Ding, et al. (2023) | China | GW | 7,779 | Laboratory values | Cardiac arrest | ETC | 0.920 | [14] |
| Lu, et al. (2023) | Taiwan | ED | 316,465 | Demographics, chief complaints, vital signs, BMI, oxygen saturation, consciousness | Cardiac arrest | RF | 0.931 | [10] |
| Wu, et al. (2024) | Taiwan | GW | 32,719 | Demographics, vital signs, laboratory values, BMI, CNS medication use | Cardiac arrest | SVM | 0.811 | [16] |
| Lee, et al. (2024) | Taiwan | GW | 114,276 | Demographics, comorbidities, presenting illness, vital signs | Cardiac arrest | SVM | 0.945 | [15] |
| Park, et al. (2025) | South Korea | GW, ICU | 62,061 | Demographics, vital signs, laboratory values, ICD-10 code | Cardiac arrest | XGBoost | 0.934 | [17] |
*Major adverse cardiac events include death, cardiac arrest, sustained ventricular tachycardia, and hypotension requiring inotropes or intra-aortic balloon pump insertion.
Abbreviations: ML, machine learning; IHCA, in-hospital cardiac arrest; AUROC, area under ROC curve; BMI, body mass index; CNS, central nervous system; ED, emergency department; ETC, extra trees classifier; GW, general ward; HRV, heart rate variability; ICU, intensive care unit; LGBM, light gradient boosting machine; LR, logistic regression; LSTM, long short-term memory; MLP, multilayer perception; RF, random forest; RNN, recurrent neural network; SVM, support vector machine; XGBoost, eXtreme Gradient Boosting; ICD-10, International Classification of Disease, Tenth Revision.
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