Table 1:

Terms and definitions

Term	Definition
Accuracy	$\frac{(\mathit{TP}+\mathit{TN})}{(\mathit{TP}+\mathit{FP}+\mathit{FN}+\mathit{TN})}$ Where TP is true positive; TN is true negative; FP is false positive; and FN is false negative.
Artificial intelligence	A process through which machines mimic “cognitive” functions that humans associate with other human minds, such as language comprehension.
Area under the curve (AUC)	A metric of binary classification; range from 0 to 1, 0 being always wrong, 0.5 representing random chance, and 1, the perfect score.
Artificial neural network	Computing systems that are inspired by, but not necessarily identical to, the biological neural networks that constitute human brain.
Attribute	Facts, details or characteristics of an entity.
Autoencoder	A class of artificial neural networks.
Concept mapping	A diagram that depicts suggested relationships between concepts.
Convolutional neural network	A class of artificial neural networks.
Decision tree	A tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
Deep learning	A subclass of a broader family of machine learning methods based on artificial neural networks. The designation “deep” signifies multiple layers of the neural network
Entities	A person, place, thing or concept about which data can be collected. Examples in the clinical domain include diseases/disorders, signs/symptoms, procedures, medications, anatomical sites
F1 score	$\frac{(2\ast \mathit{Recall}\ast \mathit{Precision})}{(\mathit{Recall}+\mathit{Precision})}$ Values range from 0 to 1 (perfect score)
Graphics processing unit	A specialized electronic circuit designed to perform very fast calculations needed for training artificial neural networks.
K-nearest neighbors	A non-parametric method used for classification and regression in pattern recognition
Latent representation	Word representations that are not directly observed but are rather inferred through a mathematical model
Machine learning	The scientific study of algorithms and probabilistic models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead
Precision	$\frac{(\mathit{TP})}{(\mathit{TP}+\mathit{FP})}$ Where TP is true positive, and FP is false positive.
Probabilistic methods	A nonconstructive method, primarily used in combinatorics, for proving the existence of a prescribed kind of mathematical object
Recall	$\frac{(\mathit{TP})}{(\mathit{TP}+\mathit{FN})}$ Where TP is true positive, and FN is false negative.
Recurrent neural network	A class of artificial neural networks
Rule-based system	Systems involving human-crafted or curated rule sets.
Semantic representation	Ways in which the meaning of a word or sentence is interpreted.
Supervised learning	Machine learning method that infers a function from labeled training data consisting of a set of training examples.
Support vector machine	Supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
tensor	A mathematical object analogous to but more general than a vector, represented by an array of components that are functions of the coordinates of a space.
Transfer learning	A machine learning technique where a model trained on one task is re-purposed on a second related task.
Unsupervised learning	Self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels.
Word embedding	The collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.