Table 1.

Techniques and algorithms of artificial intelligence[10]

Techniques and learning algorithms	Details
Fuzzy Logic	Standard logic for the concepts of true (a numerical value of 1.0) and false (a numerical value of 0.0).
	Fuzzy logic allows for partial truth (i.e., a numerical value between 0.0 and 1.0). A comparison may be made to probability theory, where the probability of a statement being true is evaluated.
	A rule-based system primarily used in control systems, fuzzy logic approximates the presence of mild, moderate, and severe hypovolemia based on normalized values of the heart rate (HR), blood pressure, and pulse volume
Classical Machine Learning	Analogous to independent variables in logistic regression.
	Guide the algorithms in analyzing complex data such as patient demographics, vital signs, and aspects of their medical history, surgery type, and patient-controlled analgesia (PCA) doses.
	The algorithm that can be used to perform either classification (classification trees) or regression tasks (regression trees) to predict total PCA consumption.
Neural Networks	It is made up of an input layer of neurons included in features that analyze the data. These at least single hidden layer of neurons performs mathematical operations on the input data and an output layer that gives algorithms to attain a particular aim (e.g., image recognition, data classification).
	Depth of anesthesia monitoring and control of anesthesia delivery
Deep Learning	A powerful tool with which to analyze massive datasets
	It analyses all available data within the training set to determine the optimal output of the given task (e.g., object recognition from an image).
Bayesian Methods	A frequentist approach to statistics is applied, wherein hypothesis testing occurs based on the frequency of events
	Allows for both modeling of uncertainty and updating or learning repetitively as new data are made an available assessment of clinical tests.