Table 1.

Glossary of AI Related Useful Terms.

CIG	Computer interpretable guidelines (CIGs) are clinical practice guidelines formalized in a computer-based system.
Data mining	Computational process to extract information and knowledge from a large dataset and to transform it into an understandable structure.
Defuzzification	In FL, defuzzification is the process of converting a combined output of fuzzy rules into a numerical values. The input for the defuzzification process is the aggregated set and the output is a single number.
Fuzzification	In FL, fuzzification is the process of mapping numerical inputs into fuzzy inputs: degree to which the inputs belong to the respective fuzzy sets according to a membership function.
Heuristic	Exploratory algorithms that shorten the time to find a reasonably good solution that would otherwise be excessively time-consuming.
Hybrid systems	Systems which integrate a combination of AI techniques; for example, neuro-fuzzy, fuzzy-expert systems, etc.
Inference engine	Key feature of an expert system in charge of the reasoning process whereby the expert system reaches a solution based on the expert’s knowledge contained in the rule base and the facts contained in the database.
Membership function	In FL, a membership function is required to convert input parameters to a fuzzy set. These membership functions, can have different shapes. The most common are triangular shape; however bell, trapezoidal, sinusoidal, and exponential can be also used.
Metaheurisitc	Algorithmic framework that provides a set of strategies to develop heuristic algorithms. GA are examples.
Multilayer perceptron (MLP)	ANN structure for supervised learning. Neurons in the perceptron are classifiers that aggregate inputs and assign a binary value (either 0 or 1).
Naïve Bayes	Technique for classification and prediction based on the Bayes theorem. The algorithm constructs models that estimate the posterior probability of each class, given a set of input attributes. Bayes’s theorem, which allows calculate the probability of A given B, from knowing the probability of observing event B given that A is true, and the probabilities of A and B. The term “Naïve” refers to the assumption that given a class, all the features or attributes are conditionally independent of each other. That premise simplify very much the calculations.
Ontology	Describes the objects, concepts and their relationships in a domain of knowledge.
Stochastic	A stochastic program operates using probabilistic methods to solve problems
Supervised learning	Mathematical algorithm able to learn from a dataset where the desired output is already known. It generalizes a function that maps the available inputs to their corresponding desired output. Classification and prediction algorithms are supervised learning algorithms.
Unsupervised learning	Algorithm devoted to discover relationships or structures in a dataset. The desired output is unknown. Examples of unsupervised algorithms are clustering and association.