Literature DB >> 32994368

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

Stephen D Auger1, Benjamin M Jacobs2,3, Ruth Dobson2,3, Charles R Marshall2,3, Alastair J Noyce2,3.   

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. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  Neuroradiology; clinical neurology; evidence-based neurology; health policy & practice; image analysis

Year:  2020        PMID: 32994368      PMCID: PMC7841474          DOI: 10.1136/practneurol-2020-002688

Source DB:  PubMed          Journal:  Pract Neurol        ISSN: 1474-7758


  22 in total

1.  Deep learning-based electroencephalography analysis: a systematic review.

Authors:  Yannick Roy; Hubert Banville; Isabela Albuquerque; Alexandre Gramfort; Tiago H Falk; Jocelyn Faubert
Journal:  J Neural Eng       Date:  2019-08-14       Impact factor: 5.379

2.  Deep reinforcement learning for automated radiation adaptation in lung cancer.

Authors:  Huan-Hsin Tseng; Yi Luo; Sunan Cui; Jen-Tzung Chien; Randall K Ten Haken; Issam El Naqa
Journal:  Med Phys       Date:  2017-11-14       Impact factor: 4.071

3.  Efficient Epileptic Seizure Prediction Based on Deep Learning.

Authors:  Hisham Daoud; Magdy A Bayoumi
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2019-07-17       Impact factor: 3.833

4.  Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury.

Authors:  Jan Claassen; Kevin Doyle; Adu Matory; Caroline Couch; Kelly M Burger; Angela Velazquez; Joshua U Okonkwo; Jean-Rémi King; Soojin Park; Sachin Agarwal; David Roh; Murad Megjhani; Andrey Eliseyev; E Sander Connolly; Benjamin Rohaut
Journal:  N Engl J Med       Date:  2019-06-27       Impact factor: 91.245

5.  Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

Authors:  Dan Milea; Raymond P Najjar; Jiang Zhubo; Daniel Ting; Caroline Vasseneix; Xinxing Xu; Masoud Aghsaei Fard; Pedro Fonseca; Kavin Vanikieti; Wolf A Lagrèze; Chiara La Morgia; Carol Y Cheung; Steffen Hamann; Christophe Chiquet; Nicolae Sanda; Hui Yang; Luis J Mejico; Marie-Bénédicte Rougier; Richard Kho; Tran Thi Ha Chau; Shweta Singhal; Philippe Gohier; Catherine Clermont-Vignal; Ching-Yu Cheng; Jost B Jonas; Patrick Yu-Wai-Man; Clare L Fraser; John J Chen; Selvakumar Ambika; Neil R Miller; Yong Liu; Nancy J Newman; Tien Y Wong; Valérie Biousse
Journal:  N Engl J Med       Date:  2020-04-14       Impact factor: 91.245

6.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

Authors:  U Rajendra Acharya; Shu Lih Oh; Yuki Hagiwara; Jen Hong Tan; Hojjat Adeli
Journal:  Comput Biol Med       Date:  2017-09-27       Impact factor: 4.589

7.  A distributional code for value in dopamine-based reinforcement learning.

Authors:  Will Dabney; Zeb Kurth-Nelson; Matthew Botvinick; Naoshige Uchida; Clara Kwon Starkweather; Demis Hassabis; Rémi Munos
Journal:  Nature       Date:  2020-01-15       Impact factor: 49.962

8.  Machine translation of cortical activity to text with an encoder-decoder framework.

Authors:  Joseph G Makin; David A Moses; Edward F Chang
Journal:  Nat Neurosci       Date:  2020-03-30       Impact factor: 24.884

9.  Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population.

Authors:  Anna Fry; Thomas J Littlejohns; Cathie Sudlow; Nicola Doherty; Ligia Adamska; Tim Sprosen; Rory Collins; Naomi E Allen
Journal:  Am J Epidemiol       Date:  2017-11-01       Impact factor: 4.897

10.  Clinically applicable deep learning for diagnosis and referral in retinal disease.

Authors:  Jeffrey De Fauw; Joseph R Ledsam; Bernardino Romera-Paredes; Stanislav Nikolov; Nenad Tomasev; Sam Blackwell; Harry Askham; Xavier Glorot; Brendan O'Donoghue; Daniel Visentin; George van den Driessche; Balaji Lakshminarayanan; Clemens Meyer; Faith Mackinder; Simon Bouton; Kareem Ayoub; Reena Chopra; Dominic King; Alan Karthikesalingam; Cían O Hughes; Rosalind Raine; Julian Hughes; Dawn A Sim; Catherine Egan; Adnan Tufail; Hugh Montgomery; Demis Hassabis; Geraint Rees; Trevor Back; Peng T Khaw; Mustafa Suleyman; Julien Cornebise; Pearse A Keane; Olaf Ronneberger
Journal:  Nat Med       Date:  2018-08-13       Impact factor: 53.440

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  4 in total

Review 1.  Artificial Intelligence shaping the future of neurology practice.

Authors:  P W Vinny; V Y Vishnu; M V Padma Srivastava
Journal:  Med J Armed Forces India       Date:  2021-07-01

Review 2.  Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.

Authors:  Ruggiero Seccia; Silvia Romano; Marco Salvetti; Andrea Crisanti; Laura Palagi; Francesca Grassi
Journal:  Life (Basel)       Date:  2021-02-05

Review 3.  Challenges in Clinicogenetic Correlations: One Phenotype - Many Genes.

Authors:  Rahul Gannamani; Sterre van der Veen; Martje van Egmond; Tom J de Koning; Marina A J Tijssen
Journal:  Mov Disord Clin Pract       Date:  2021-03-02

Review 4.  Domotics, Smart Homes, and Parkinson's Disease.

Authors:  Cristina Simonet; Alastair J Noyce
Journal:  J Parkinsons Dis       Date:  2021       Impact factor: 5.568

  4 in total

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