Repository logo
  • Log In
    Log in via Symplectic to deposit your publication(s).
Repository logo
  • Communities & Collections
  • Research Outputs
  • Statistics
  • Log In
    Log in via Symplectic to deposit your publication(s).
  1. Home
  2. Faculty of Medicine
  3. National Heart and Lung Institute
  4. National Heart and Lung Institute
  5. Artificial intelligence, data sensors and interconnectivity: future Opportunities for heart failure
 
  • Details
Artificial intelligence, data sensors and interconnectivity: future Opportunities for heart failure
File(s)
Artificial Intelligence, Data Sensors and Interconnectivity Future Opportunities for Heart Failure.pdf (217.53 KB)
Published version
Author(s)
Bachtiger, Patrik
Plymen, Carla M
Pabari, Punam A
Howard, James P
Whinnett, Zachary I
more
Type
Journal Article
Abstract
A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors - so-called interconnectivity - and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.
Date Issued
2020-03-01
Date Acceptance
2020-01-23
Citation
Cardiac Failure Review, 2020, 6, pp.e11-e11
URI
http://hdl.handle.net/10044/1/79991
DOI
https://www.dx.doi.org/10.15420/cfr.2019.14
ISSN
2057-7540
Publisher
Radcliffe Cardiology
Start Page
e11
End Page
e11
Journal / Book Title
Cardiac Failure Review
Volume
6
Copyright Statement
© Radcliffe Cardiology 2020. This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/32514380
Subjects
Artificial intelligence
connected care
data sensors
deep learning
heart failure
machine learning
remote monitoring
Publication Status
Published
Coverage Spatial
England
About
Spiral Depositing with Spiral Publishing with Spiral Symplectic
Contact us
Open access team Report an issue
Other Services
Scholarly Communications Library Services
logo

Imperial College London

South Kensington Campus

London SW7 2AZ, UK

tel: +44 (0)20 7589 5111

Accessibility Modern slavery statement Cookie Policy

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback