This paper is in the following e-collection/theme issue:

Telehealth and Telemonitoring (1671) Clinical Information and Decision Making (1407) Machine Learning (1465)

Published on in Vol 20, No 9 (2018): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9227, first published .
Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data

Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data

Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data

Authors of this article:

Peter Orchard1 Author Orcid Image ;   Anna Agakova1 Author Orcid Image ;   Hilary Pinnock2 Author Orcid Image ;   Christopher David Burton3 Author Orcid Image ;   Christophe Sarran4 Author Orcid Image ;   Felix Agakov1 Author Orcid Image ;   Brian McKinstry2 Author Orcid Image
Article Authors Cited by (37) Tweetations (62) Metrics

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  18. Wang K, Gu L, Liu W, Xu C, Yin C, Liu H, Rong L, Li W, Wei X. The predictors of death within 1 year in acute ischemic stroke patients based on machine learning. Frontiers in Neurology 2023;14 View
  19. Pinnock H, Hui C, van Boven J. Implementation of digital home monitoring and management of respiratory disease. Current Opinion in Pulmonary Medicine 2023;29(4):302 View
  20. Cascarano A, Mur-Petit J, Hernández-González J, Camacho M, de Toro Eadie N, Gkontra P, Chadeau-Hyam M, Vitrià J, Lekadir K. Machine and deep learning for longitudinal biomedical data: a review of methods and applications. Artificial Intelligence Review 2023;56(S2):1711 View
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