L
Lionel Tarassenko
Researcher at University of Oxford
Publications - 419
Citations - 19351
Lionel Tarassenko is an academic researcher from University of Oxford. The author has contributed to research in topics: Artificial neural network & Vital signs. The author has an hindex of 67, co-authored 395 publications receiving 16265 citations. Previous affiliations of Lionel Tarassenko include National Institutes of Health & National Institute for Health Research.
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Journal ArticleDOI
Trajectories of vital signs in patients with COVID-19.
Marco A. F. Pimentel,Oliver C. Redfern,Robert Hatch,J Duncan Young,Lionel Tarassenko,Peter J. Watkinson +5 more
TL;DR: Patients with COVID-19 who deteriorate in hospital experience rapidly-worsening respiratory failure, with low SpO2 and high FiO2, but only minor abnormalities in other vital signs, which has potential implications for the ability of early warning scores to identify deteriorating patients.
Journal ArticleDOI
Early risk assessment for COVID-19 patients from emergency department data using machine learning.
Frank S. Heldt,Marcela P. Vizcaychipi,Marcela P. Vizcaychipi,Sophie Peacock,Mattia Cinelli,Lachlan McLachlan,Fernando Andreotti,Stojan Jovanović,Robert Dürichen,Nadezda Lipunova,Robert A. Fletcher,Anne Hancock,Alex J. McCarthy,Richard A. Pointon,Alexander Brown,James Eaton,Roberto Liddi,Lucy Mackillop,Lucy Mackillop,Lionel Tarassenko,Lionel Tarassenko,Rabia T. Khan +21 more
TL;DR: In this article, the authors analyzed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England between January 1st and May 26th, 2020, with a majority of cases occurring in March and April.
Proceedings ArticleDOI
Non-Contact Vital Sign Monitoring in the Clinic
TL;DR: Using an off-the-shelf camera, estimates of heart rate and respiratory rate are computed, and changes in peripheral oxygen saturation are detected in a real hospital scenario, without interfering with regular patient care.
Proceedings ArticleDOI
Testing of Wearable Monitors in a Real-World Hospital Environment: What Lessons Can Be Learnt?
TL;DR: Four commercially-available, CE-marked sensors were combined into three monitoring systems and used to record the electrocardiograms and photoplethysmograms of 31 hospitalised patients, to determine whether the sensors could collect vital sign data reliably enough for use in larger clinical trials.
Journal ArticleDOI
A Large-Scale Clinical Validation of an Integrated Monitoring System in the Emergency Department
David A. Clifton,David Wong,Lei Clifton,Sarah J. Wilson,Rob Way,Richard Pullinger,Lionel Tarassenko +6 more
TL;DR: It is demonstrated that machine learning technologies embedded within healthcare information systems can provide clinical benefit, with the potential to improve patient outcomes in the busy environment of a major ED and other high-dependence areas of patient care.