Predicting patient deterioration with physiological data using AI: systematic review protocol

Ethical approval is not required as data will be obtained from published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.

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Publication Details

Journal
BMJ Health & Care Informatics
Published
2025-08-01
DOI
https://doi.org/10.1136/bmjhci-2024-101417
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Predicting patient deterioration with physiological data using AI: systematic review protocol

Chris Plummer, Edward Meinert, Lynsey Threlfall, Cen Cong et al.
1 links
BMJ Health & Care Informatics
Sepsis Diagnosis and Treatment
article

Predicting patient deterioration with physiological data using AI: systematic review protocol

Chris Plummer, Edward Meinert, Lynsey Threlfall, Cen Cong, Victoria Riccalton
article en

Abstract

Ethical approval is not required as data will be obtained from published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.

BMJ Health & Care InformaticsVol. 32(1)
Newcastle upon Tyne Hospitals NHS Foundation Trust (GB), NIHR Newcastle Biomedical Research Centre (GB), Imperial College London (GB), Newcastle University (GB)
National Institute for Health and Care Research, Newcastle University, Newcastle upon Tyne Hospitals NHS Foundation Trust, NIHR Newcastle Biomedical Research Centre
Good health and well-being
Openalex Percentile: Top 72%
Sepsis Diagnosis and Treatment
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Predicting patient deterioration with physiological data using AI: systematic review protocol — Chris Plummer, Edward Meinert, et al. · BMJ Health & Care Informatics (2025) | TGRS Research Map | TGRS