Table 1:
Summary of literature reviews
| Year | Reference | Description | Methodology used | Results obtained | Dataset used | Significance | Research gaps |
|---|---|---|---|---|---|---|---|
| 2025 | Yang et al. [29] | Early sepsis prediction | XGBoost | Early prediction using vital signs | Multi-center dataset | Supports early detection | Limited temporal information |
| 2025 | Kim et al. [30] | Temporal dynamic embedding | TDE neural architecture. | TDE performed competitively | PhysioNet 2019 | Avoids heavy imputation | Require careful tuning on very sparse variables. |
| 2025 | Almasoud et al. [31] | Hybrid DL model | CNN | AUC—0.998 | PhysioNet 2019 | Improved decision-making | Limit the generalizability of the results |
| 2024 | Gao et al. [32] | Sepsis mortality prediction | Machine learning | Mortality risk prediction | ICU patient data | Supports mortality risk assessment | Requires broader validation |
| 2024 | Boussina et al. [33] | Deployment of a DL sepsis alert | Feed forward neural network | AUROC—0.945 | UC San Diego Health System | Significant reduction in mortality | Confounding biases |
| 2024 | Zheng et al. [34] | scCaT capsule + Transformer | Capsule networks + Transformer decoder | AUROC—0.93 | RNA data used | Offers biologically interpretable features | May not directly translate to clinical early warning |
| 2023 | Solís-García et al. [35] | Experimental comparison of multiple ML/AI, | Classical ML and DL models | AUROC—0.93 | Clinical ICU datasets | Preprocessing model rankings | Conclusions dependent on the chosen |
| 2023 | Saleena et al. [36] | Summarizing ML/DL models | Narrative synthesis of model types, datasets | AUROC—0.93 | MIMIC, PhysioNet | Synthesizes gaps | Heterogeneity of primary studies |
| 2023 | Ramirez-Alcocer et al. [37] | DL for predictive process | DL sequence models (RNNs/transformers) | Precision—0.92 Recall—0.90 | Healthcare event-sequence datasets) | Demonstrates utility of DL for process | Focus is on process monitoring workflow events |
| 2022 | Apalak et al. [38] | Conditional recurrent | Recurrent adversarial network architecture | AUROC—0.94 | MIMIC dataset | Generalization in sepsis | Adversarial training |
| 2022 | Chen et al. [39] | Comparative study of 6 ML methods | Decision trees, SVM, ensemble methods | XGBoost model accuracy—0.79 | BupaR package | Highlights model differences | Limited external validation |
| 2021 | Kuo et al. [40] | ANN models using real-world EHR data. | Artificial Neural Networks | AUC—0.786 | Real-world hospital EHR | Models can be robust without artificial imputation | May reduce predictive performance |
| 2021 | Shashikumar et al. [41] | DeepAISE: a recurrent neural survival model | Recurrent survival modeling RNN + survival analysis | AUC—0.89 | MIMIC | To predict both event risk and timing | Complexity and survival loss |
| 2021 | Aşuroğlu et al. [42] | To predict SOFA-based sepsis severity | CNNs, RF | AUC—0.982 | MIMIC-III ICU dataset | The approach aligns with the need for real-time | May not generalize to different ICU settings |
| 2020 | Burdick et al. [43] | Retrospective validation of an ML algorithm | ML algorithm | AUROC 0.93 | Dataset from 461 US hospitals | Demonstrates model generalizability | Heterogeneity in labeling |
| 2020 | Pettinati et al. [44] | Practical ML approaches focusing on | ML pipelines, feature engineering | AUROC—0.56 | MIMIC 3 dataset | Practical considerations | Limited external evaluation details |
| 2020 | Lauritsen et al. [45] | Uses DL on EHR event sequences. | Sequence models RNN/transformer-style | AUROC—0.75 | Multiple Danish hospitals | Advantages of event-sequence modeling | Transferability depends on EHR event logging |
| 2019 | Scherpf et al. [46] | Applies RNNs for model | RNN/LSTM sequence modeling | AUROC—0.81 | MIMIC-III public ICU dataset | Demonstrates applicability of RNNs | Results sensitive to sepsis labeling |
| 2018 | Wang et al. [47] | ML framework. | Ensemble ML methods with feature selection | Sensitivity—0.91 | Clinical datasets | Early contribution to ensemble approaches | Lack of large external validation |
| 2017 | Sawada et al. [48] | Transfer learning approach for DNNs. | Transfer learning methods for deep networks | Accuracy—0.90 | Actual two dimensional image | Shows value of transfer learning | Requiring further validation and broader external testing |
[i] AUC, area under the ROC curve; CNN, convolutional neural networks; DL, deep learning; DNNs, Deep Neural Networks; EHR, electronic health records; ICUs, intensive care units; LSTM, long short-term memory; ML, machine learning; RF, random forest; RNA, Ribonucleic Acid; RNN, Recurrent Neural Network; SOFA, Sequential Organ Failure Assessment; SVM, support vector machine; TDE, Temporal Dynamic Embedding.
Figure 1:
GRU-D based on time decay factor [49]. GRU-D, gated recurrent unit with decay.
Figure 2:
Distribution of sepsis onset hours.
Figure 3:
Distribution of records of time-series per patient.
Table 2:
Pseudo-ablation analysis of VAE latent dimension
| Latent dimension | Observed effect on representation | Downstream impact (GRU-D + LightGBM) |
|---|---|---|
| 2 | Over-compressed, loss of clinical variability | Reduced recall and unstable temporal learning |
| 4 (Selected) | Balanced compression preserving key physiological patterns | Stable convergence and best precision–recall balance |
| 8 | Increased redundancy in latent features | No improvement in AUROC/AUPRC; higher complexity |
| ≥16 | Over-parameterization | Increased overfitting risk without performance gains |
Table 3:
VAE reconstruction effects on features
| Feature | Original value | After VAE reconstruction | Difference | Interpretation |
|---|---|---|---|---|
| HR | 88 | 85.7 | −2.3 | Slight smoothing of variation |
| RR | 22 | 21.2 | −0.8 | Noise reduction |
| MAP | 76 | 77.1 | +1.1 | Maintains overall trend |
| Oxygen saturation (SpO2) | 94 | 93.6 | −0.4 | Small value adjustment |
| Body temperature (°C) | 37.6 | 37.4 | −0.2 | Retains clinical normality |
| SBP | 122 | 119.8 | −2.2 | Reduced measurement noise |
| DBP | 74 | 73.2 | −0.8 | Preserved pattern with smoothing |
| Glucose level (mg/dL) | 148 | 140.5 | −7.5 | Reduced variability/stabilization |
| WBC | 12.4 | 12.1 | −0.3 | Maintains infection trend |
| Lactate level (mmol/L) | 2.6 | 2.4 | −0.2 | Slight artifact reduction |
| Creatinine (mg/dL) | 1.9 | 1.8 | −0.1 | Kidney function pattern retained |
| Platelet count (× 103/µL) | 182 | 179.6 | −2.4 | Preserves clinical direction |
Figure 4:
Workflow of GRU-D and LightGBM-based sepsis prediction model. GRU-D, gated recurrent unit with decay; ICUs, intensive care units; LightGBM, Light gradient boosting machine.
Table 4:
Hyperparameter settings for GRU-D and LightGBM models
| Model | Hyperparameter | Description | Tuned range/value | Optimal value |
|---|---|---|---|---|
| GRU-D | Hidden size | Number of hidden units in GRU-D | 32, 64, 128, 256 | 128 |
| GRU-D | Number of layers | Depth of GRU-D model | 1, 2, 3 | 2 |
| GRU-D | Dropout | Dropout rate for regularization | 0.1, 0.3, 0.5 | 0.3 |
| GRU-D | Learning rate | Step size for optimizer | 1e.4, 5e.4, 1e.3 | 5e.4 |
| GRU-D | Batch size | Number of samples per batch | 32, 64, 128 | 64 |
| GRU-D | Optimizer | Optimization algorithm | Adam, RMsprop, SGD | Adam |
| LightGBM | n_estimators | Number of boosting iterations | 100, 200, 500, 1000 | 500 |
| LightGBM | max_depth | Maximum depth of tree | 3, 5, 7, −1 | 5 |
| LightGBM | learning_rate | Shrinkage rate applied to each tree | 0.01, 0.05, 0.1 | 0.05 |
| LightGBM | subsample | Fractions of samples per boosting iteration | 0.6, 0.8, 1.0 | 0.8 |
| LightGBM | colsample_bytree | Fraction of features used per iteration | 0.6, 0.8, 1.0 | 0.8 |
| LightGBM | red_lambda | L2 regularization term | 0, 0.1, 0.5, 1.0 | 0.1 |
Table 5:
GRD-D and LightGBM performance metrics
| Model | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) | ROC AUC | PR AUC |
|---|---|---|---|---|---|---|
| GRU-D | 94.39 | 81.67 | 34.62 | 48.63 | 0.90 | 0.61 |
| LightGBM | 94.98 | 77.099 | 49.29 | 59.94 | 0.92 | 0.68 |
Figure 5:
LightGBM precision-recall curve. LightGBM, Light gradient boosting machine.
Figure 6:
LightGBM ROC curve. LightGBM, Light gradient boosting machine.
Figure 7:
LightGBM confusion matrix. LightGBM, Light gradient boosting machine.
Figure 8:
GRU-D precision-recall curve. AUC, area under the ROC curve; GRU-D, gated recurrent unit with decay.
Figure 9:
GRU-D ROC curve. AUC, area under the ROC curve; GRU-D, gated recurrent unit with decay.
Figure 10:
GRU-D ROC confusion matrix. GRU-D, gated recurrent unit with decay.
Figure 11:
ROC curve comparison. AUC, area under the ROC curve; GRU-D, gated recurrent unit with decay; LightGBM, Light gradient boosting machine.
Figure 12:
Precision recall curve comparison. AUC, area under the ROC curve; GRU-D, gated recurrent unit with decay; LightGBM, Light gradient boosting machine.
Table 6:
Cross-validation performance of GRU-D, LightGBM, and hybrid models
| Model | Accuracy (mean ± std) | Precision (mean ± std) | Recall (mean ± std) | F1-score (mean ± std) | AUROC (mean ± std) |
|---|---|---|---|---|---|
| GRU-D | 0.827 ± 0.014 | 0.842 ± 0.021 | 0.815 ± 0.024 | 0.828 ± 0.019 | 0.912 ± 0.010 |
| LightGBM | 0.861 ± 0.018 | 0.835 ± 0.020 | 0.808 ± 0.026 | 0.821 ± 0.022 | 0.905 ± 0.012 |
| Hybrid (GRU-D + LGBM) | 0.888 ± 0.011 | 0.857 ± 0.017 | 0.845 ± 0.020 | 0.851 ± 0.018 | 0.931 ± 0.009 |
Figure 13:
Top 20 important features from LightGBM model. GRU-D, gated recurrent unit with decay; LightGBM, Light gradient boosting machine.
Figure 14:
Feature correlation heatmap of clinical variables. DBP, diastolic blood pressure; HR, heart rate; MAP, mean arterial pressure; SBP, systolic blood pressure; WBC, white blood cell count.
Figure 15:
GRU-D embedding heatmap representing learned latent features. GRU-D, gated recurrent unit with decay.