Effect of Digital Early Warning Scores on Hospital Vital Sign Observation Protocol Adherence: Stepped-Wedge Evaluation.

Autor: Wong DC; Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, United Kingdom., Bonnici T; Critical Care Division, University College Hospital London NHS Foundation Trust, London, United Kingdom., Gerry S; Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom., Birks J; Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom., Watkinson PJ; Oxford University Hospitals NHS Trust, Oxford, United Kingdom.; NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.; Nuffield Department of Clinical Neurosciences, Kadoorie Centre for Critical Care Research and Education, University of Oxford, Oxford, United Kingdom.

Publikováno v: Journal of medical Internet research [J Med Internet Res] 2024 Jun 20; Vol. 26, pp. e46691. Date of Electronic Publication: 2024 Jun 20.

An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram

Autor: Charlton, PH, Bonnici, T, Tarassenko, L, Clifton, D, Beale, R, Watkinson, PJ

Publikováno v: Charlton, P, Bonnici, T A, Tarassenko, L, Clifton, D, Beale, R & Watkinson, P 2016, ' An Assessment of Algorithms to Estimate Respiratory Rate from the Electrocardiogram and Photoplethysmogram ', Physiological Measurement, vol. 37, no. 4, pp. 610-626 . https://doi.org/10.1088/0967-3334/37/4/610
Physiological Measurement
Charlton, P H, Bonnici, T A, Tarassenko, L, Clifton, D, Beale, R & Watkinson, P 2016, An Assessment of Algorithms to Estimate Respiratory Rate from the Electrocardiogram and Photoplethysmogram . in MEIBioeng16 . MEIbioeng 16, United Kingdom, 5/09/2016 .

ObjectivesOver 100 algorithms have been proposed to estimate respiratory rate (RR) from the electrocardiogram (ECG) and photoplethysmogram (PPG). As they have never been compared systematically it is unclear which algorithm performs the best. Our pri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::e6cffdcfc6a631227e8e3dc3e8bcb114
https://doi.org/10.1088/0967-3334/37/4/610

Clinical deployment environments: Five pillars of translational machine learning for health.

Autor: Harris S; Institute of Health Informatics, University College London, London, United Kingdom.; Department of Critical Care, University College London Hospital, London, United Kingdom., Bonnici T; Institute of Health Informatics, University College London, London, United Kingdom.; Department of Critical Care, University College London Hospital, London, United Kingdom., Keen T; Institute of Health Informatics, University College London, London, United Kingdom., Lilaonitkul W; Institute of Health Informatics, University College London, London, United Kingdom., White MJ; Digital Healthcare, University College London Hospital, London, United Kingdom., Swanepoel N; Centre for Advanced Research Computing, University College London, London, United Kingdom.

Publikováno v: Frontiers in digital health [Front Digit Health] 2022 Aug 19; Vol. 4, pp. 939292. Date of Electronic Publication: 2022 Aug 19 (Print Publication: 2022).

Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review.

Autor: Alhmoud B; Institute of Health Informatics, University College London, London, UK., Bonnici T; Institute of Health Informatics, University College London, London, UK.; University College London Hospitals NHS Trust, London, UK., Patel R; University College London Hospitals NHS Trust, London, UK.; Institute of Cardiovascular Science, University College London, London, UK.; Barts Health NHS Trust, London, UK., Melley D; Barts Health NHS Trust, London, UK., Williams B; University College London Hospitals NHS Trust, London, UK.; Institute of Cardiovascular Science, University College London, London, UK., Banerjee A; Institute of Health Informatics, University College London, London, UK ami.banerjee@ucl.ac.uk.; University College London Hospitals NHS Trust, London, UK.; Barts Health NHS Trust, London, UK.

Publikováno v: BMJ open [BMJ Open] 2021 Apr 08; Vol. 11 (4), pp. e045849. Date of Electronic Publication: 2021 Apr 08.

Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies.

Autor: Nanini, Santino^1,2,3,4,5 (AUTHOR) mariem.abid@applicare.ai, Abid, Mariem^1,2 (AUTHOR), Mamouni, Yassir^3,5 (AUTHOR), Wiedemann, Arnaud^1,6,7 (AUTHOR) arnaud.wiedemann.med@ssss.gouv.qc.ca, Jouvet, Philippe^1,6,7 (AUTHOR) philippe.jouvet.med@ssss.gouv.qc.ca, Bourassa, Stephane^1,4,6,7,8 (AUTHOR)

Publikováno v: Diagnostics (2075-4418). Dec2024, Vol. 14 Issue 23, p2763. 40p.