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.

Table 2 List of currently available neural network models

Model	Variations	Use cases	Strengths	Weaknesses
Multilayer perceptron	N/A	Classification and regression tasks	Simple architecture and good for baseline models	Not ideal for spatial or sequential data. Can overfit with high dimensional data
Convolutional neural networks	AlexNet	Image recognition	Captures spatial hierarchies	Computationally intensive
	VGGNet	Object detection	Effective for image processing	Requires large datasets
	ResNet	Complex computer vision tasks	Residual learning avoids vanishing gradient	Requires higher computation
	Inception	Image recornition with lower computations	Efficient use of resources	Architecture complexity
	MobileNet	Mobile and embedded vision applications	Lightweight and efficient	Trade-off in accuracy for efficiency
RNN	LSTM	Language modeling	Handles sequential data	Vanishing gradient problem
	Gated recurrent unit	Time series forecasting	Simplified version of LSTM	Less powerful for complex tasks
	Bidirectional RNN	Speech recognition	Considers past and future context	Computationally expensive
GAN	DCGAN	Image generation	Generates high quality data	Training instability
	CycleGAN	Unsupervised image-to-image translation	Advances data augmentation	Mode collapse issues
	StyleGAN	Synthetic image creation for design tasks	Generates photorealistic images	Computationally expensive
Autoencoders	Variational autoencoders	Dimensionality reduction, generative tasks	Effective for feature extraction	Blurry reconstructions
	Denoising autoencoders	Anomaly detection and noise reduction	Robust against noisy inputs	Limited generative capability
Transformers	Bidirectional Encoder Representations from Transformers	Contextual embeddings for natural language processing tasks	Captures long-range dependencies	High computational requirements
	General Purpose Transformers series	Generative tasks (e.g. text generation)	Powerful generative abilities	Requires vast amounts of training data
	T5	Text summarization, translation	Task-agnostic and flexible	Computationally intensive
Graph neural networks	Graph convolutional networks	Social network analysis, biological modeling	Handles graph-structured data	Scalability issues
	Graph attention networks	Recommendation systems	Captures relational information	Complex architecture
	GraphSAGE	Molecular modeling, protein interactions	Effective for inductive learning	Requires large-scale graph sampling
Self organizing maps	N/A	Data visualization	Intuitive mapping and visualization	Less effective for high-dimensional data
Boltzmann machines	Restricted boltzmann machines	Collaborative filtering, dimensionality reduction	Probabilistic feature learning	Difficult to train
	Deep belief networks	Feature learning and pretraining	Effective for unsupervised learning	Computationally expensive
Deep reinforcement learning models	Deep Q networks	Game playing (e.g., AlphaGo)	Learns optimal policies	Sample inefficiency
	Proximal policy optimization	Robotics, autonomous navigation	Handles high-dimensional inputs	Requires hyperparameter tuning
	Actor critic methods	Autonomous systems	Balances policy and value learning	May require extensive exploration

GAN: Generative adversarial networks; LSTM: Long short-term memory; RNN: Recurrent neural networks; N/A: Not applicable.