Big data, machine learning and artificial intelligence: a neurologist's guide

被引:22
|
作者
Auger, Stephen D. [1 ]
Jacobs, Benjamin M. [1 ,2 ]
Dobson, Ruth [1 ,2 ]
Marshall, Charles R. [1 ,2 ]
Noyce, Alastair J. [1 ,2 ]
机构
[1] Queen Mary Univ London, Wolfson Inst Prevent Med, Prevent Neurol Unit, London, England
[2] Royal London Hosp, Dept Neurol, London, England
关键词
Neuroradiology; image analysis; health policy & practice; evidence-based neurology; clinical neurology; ALGORITHM; HEALTH; GO;
D O I
10.1136/practneurol-2020-002688
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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.
引用
收藏
页码:4 / 11
页数:8
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