Artificial Intelligence in medicine: Modeling methods (Part I)

Abstract

The emergence of artificial intelligence and machine learning in medicine determines that healthcare professionals should understand generalities of their methodologies. This narrative review consists of two parts. The first consists of an exploration of the main methods used to model in machine learning, described in a simple way by medical and mathematical authors, with the purpose to bring this methodology healthcare workers. Here we will describe the basic structure of a machine learning algorithm (input information, task to execute, output result, optimization, and adjustment), its main classifications (supervised, unsupervised and by reinforcement) and the main modeling methods used. We will review regression and then explore decision trees, support vector machines, principal component analysis, clustering, K-means, hierarchical clustering, deep learning, and convolutional neural networks. In this way, we hope to bring this methodology closer to healthcare personnel to increase the interpretability of the published work in medicine that use these methodologies.