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

Peter Orchard   1 , MSci, MSc, PhD ;   Anna Agakova   1 , MSc (Comp Sci), MEng ;   Hilary Pinnock   2 , MBChB, MD, MRCGP ;   Christopher David Burton   3 , MBChB, MD, FRCGP ;   Christophe Sarran   4 , MPhys, PhD ;   Felix Agakov   1 , MSc, PhD ;   Brian McKinstry   2 , MBChB, MD, FRCP(Edin), FRCGP, FFCI

1 Pharmatics, Edinburgh, United Kingdom

2 Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom

3 Academic Unit of Primary Medical Care, University of Sheffield, Sheffield, United Kingdom

4 Met Office, Exeter, United Kingdom

Corresponding Author:

  • Brian McKinstry, MBChB, MD, FRCP(Edin), FRCGP, FFCI
  • Usher Institute of Population Health Sciences and Informatics
  • University of Edinburgh
  • Number Nine Bioquarter
  • 9 Little France Road
  • Edinburgh, EH16 4UX
  • United Kingdom
  • Phone: 44 1316502378
  • Email: brian.mckinstry@ed.ac.uk