Noyce, A;
(2021)
Big data, machine learning and artificial intelligence: a neurologist’s guide.
Practical Neurology
, 21
(1)
pp. 4-11.
10.1136/practneurol-2020-002688.
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Abstract
Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.
| Type: | Article |
|---|---|
| Title: | Big data, machine learning and artificial intelligence: a neurologist’s guide |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1136/practneurol-2020-002688 |
| Publisher version: | https://doi.org/10.1136/practneurol-2020-002688 |
| Language: | English |
| Additional information: | This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10108507 |
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