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  • Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study

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Neurology
Original research
Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study

Authors

  • Anna Markella Antoniadi UCD School of Computer Science, University College Dublin, Dublin, IrelandFutureNeuro SFI Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland PubMed articlesGoogle scholar articles
  • Miriam Galvin Academic Unit of Neurology, Trinity Biomedical Sciences Institute, University of Dublin Trinity College, Dublin, Ireland PubMed articlesGoogle scholar articles
  • Mark Heverin Academic Unit of Neurology, Trinity Biomedical Sciences Institute, University of Dublin Trinity College, Dublin, Ireland PubMed articlesGoogle scholar articles
  • Orla Hardiman FutureNeuro SFI Research Centre, Royal College of Surgeons in Ireland, Dublin, IrelandAcademic Unit of Neurology, Trinity Biomedical Sciences Institute, University of Dublin Trinity College, Dublin, IrelandDepartment of Neurology, National Neuroscience Centre, Beaumont Hospital, Dublin, Ireland PubMed articlesGoogle scholar articles
  • Catherine Mooney UCD School of Computer Science, University College Dublin, Dublin, IrelandFutureNeuro SFI Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Catherine Mooney; catherine.mooney{at}ucd.ie
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Citation

Antoniadi AM, Galvin M, Heverin M, et al
Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study
BMJ Open 2020;10:e033109. doi: 10.1136/bmjopen-2019-033109

Publication history

  • Received August 12, 2019
  • Revised February 5, 2020
  • Accepted February 7, 2020
  • First published February 28, 2020.
Online issue publication 
February 14, 2023
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© 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. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.