Supervised machine learning tools: a tutorial for clinicians. (19th November 2020)

• Record Type: Journal Article
• Authors: Lo Vercio, Lucas; Amador, Kimberly; Bannister, Jordan J; Crites, Sebastian; Gutierrez, Alejandro; MacDonald, M. Ethan; Moore, Jasmine; Mouches, Pauline; Rajashekar, Deepthi; Schimert, Serena; Subbanna, Nagesh; Tuladhar, Anup; Wang, Nanjia; Wilms, Matthias; Winder, Anthony; Forkert, Nils D
• Is Part Of: Journal of neural engineering. Volume 17:Number 6(2020:Dec.)
• Publication Date: 2020-11-19
• Subjects: machine learning -- artificial intelligence -- classification -- regression -- deep learning; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/abbff2
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.