Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data

Orchard, P., Agakova, A., Pinnock, H. orcid.org/0000-0002-5976-8386 et al. (4 more authors) (2018) Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data. Journal of Medical Internet Research, 20 (9). e263. ISSN 1439-4456

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © Peter Orchard, Anna Agakova, Hilary Pinnock, Christopher David Burton, Christophe Sarran, Felix Agakov, Brian McKinstry. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.09.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Keywords: Machine learning; telemedicine; chronic obstructive pulmonary disease
Dates:
  • Accepted: 18 June 2018
  • Published (online): 21 September 2018
  • Published: 21 September 2018
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine (Sheffield) > Academic Unit of Medical Education (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 25 Jul 2018 10:25
Last Modified: 27 Sep 2018 10:47
Published Version: https://doi.org/10.2196/jmir.9227
Status: Published
Publisher: Journal of Medical Internet Research
Refereed: Yes
Identification Number: https://doi.org/10.2196/jmir.9227

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