Predicting outcomes using neural networks in the intensive care unit

Table 1 Neural network models discussed in the manuscript

Model/scoring system	Primary use case	Strengths	Limitations
Convolutional neural networks	Image-based tasks (e.g., computed tomography scans and X-rays)	High accuracy in spatial feature extraction	Computationally expensive
Recurrent neural networks	Time-series predictions (e.g., sepsis progression)	Captures temporal dependencies effectively	Potentially high computational cost
Multilayer perceptron	Nonlinear relationship modeling (e.g., ICU mortality)	Flexible, integrates with hybrid systems	Prone to overfitting if not regularized
Balanced random forests	Handling imbalanced datasets	Interpretable, robust to class imbalance	Requires careful tuning of hyperparameters
Sequential Organ Failure Assessment	Assessing organ failure severity	Widely validated, clinically interpretable	Limited to scoring; no predictive modeling
Acute Physiology and Chronic Health Evaluation	Evaluating ICU patient mortality risk	Comprehensive, includes chronic health factors	Limited in real-time adaptability

ICU: Intensive care unit.