Table 1.
Characteristics of studies aimed at prediction of return of spontaneous circulation outcomes and survival. ROSC = return of spontaneous circulation.
|
Prediction of ROSC and Survival (n = 15) | |
|---|---|
| Dates | Frequency (%) |
| 2023 | 1 (7) |
| 2022 | 5 (34) |
| 2021 | 4 (27) |
| 2020 | 2 (14) |
| 2019 | 2 (14) |
| 2009 | 1 (7) |
| Country of Study Origin | |
| Hong Kong | 1 (7) |
| Japan | 2 (14) |
| Korea | 3 (20) |
| Singapore | 1 (7) |
| Slovenia | 1 (7) |
| Taiwan | 1 (7) |
| United States | 2 (14) |
| Multinational Collaboration | 4 (27) |
| Patient Demographic | |
| Adults (<18 years) | 10 (67) |
| Adults & Pediatrics | 1 (7) |
| Not Specified | 4 (27) |
| AI Branch | |
| Machine Learning | 11 |
| Machine Learning & Deep Learning | 4 |
| Classification Algorithm | |
| Artificial Neural Networks | 1 |
| Autoscore | 2 |
| Classification and Regression Tree | 1 |
| Decision Tree | 4 |
| Deep Neural Networks | 2 |
| Embedded Fully Convolutional Network | 2 |
| Gradient Boosting | 2 |
| K-Nearest Neighbor | 5 |
| Lagrangian Support Vector Machine | 1 |
| LightGBM | 4 |
| Logistic Regression | 7 |
| Multilayer Perceptron | 3 |
| Naïve Bayes | 1 |
| Neural Networks | 1 |
| Random Forest | 15 |
| Reduced Support Vector Machine | 1 |
| Regularized Logistic Regression | 1 |
| Support Vector Machine | 6 |
| Voting Classifier | 1 |
| XGBoost | 4 |