Artificial intelligence in upper gastrointestinal bleeding: Can machine learning predict endotherapy requirements?

Table 1 Machine learning models for the prediction of gastrointestinal bleed

Supervised machine learning
Classification task	Comment	Regression task	Comment
K-nearest neighbours	Data classification as per k-nearest neighbours, non-parametric	Gradient boosting model	A combination of weaker models (e.g., a decision tree) to create a stronger prediction model
		XGBoost
		LightGBM
		CatBoost
Neural network	Deep learning method composed of interconnected layers of artificial neurons	Support vector machine	Technique to categorise data points by finding an optimal hyperplane
ANN
CNN
Decision tree	An arranged tree in which internal nodes are attributes, branches are decisions, and leaves are outcomes or labels	Regression analysis	Useful for predicting time-to-event outcomes, including covariates and event times, such as bleeding recurrence

XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; CatBoost: Categorical boosting; ANN: Artificial neural network; CNN: Convolutional neural network.