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Supervised machine learning tools: a tutorial for clinicians

Publication Type : Journal Article

Thematic Areas : Wireless Network and Application

Publisher : Journal of Neural Engineering

Source : Journal of Neural Engineering, 17(6), October 2020

Url :

Keywords : artificial intelligence; classification; deep learning; machine learning; regression.

Campus : Amritapuri

School : School of Engineering

Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)

Department : Wireless Networks and Applications (AWNA)

Year : 2020

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.

Cite this Research Publication : Lucas Lo Vercio, Kimberly Amador, Jordan J Bannister, Sebastian Crites, Alejandro Gutierrez, M Ethan MacDonald, Jasmine Moore, Pauline Mouches, Deepthi Rajashekar, Serena Schimert, Nagesh Subbanna, Anup Tuladhar, Nanjia Wang, Matthias Wilms, Anthony Winder, Nils D Forkert, "Supervised machine learning tools: a tutorial for clinicians", accepted for publication at Journal of Neural Engineering (invited paper), 2020. DOI: 10.1088/1741-2552/abbff2

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