Articles
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Dec 5, 2024 |
arxiv.org | Yichen Huang |Soheila Molaei |Yujiang Wang |David A Clifton
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Nov 18, 2024 |
nature.com | Jia Wei |Jiandong Zhou |Kevin Yuan |David A Clifton
AbstractAccurately predicting hospital discharge events could help improve patient flow and the efficiency of healthcare delivery. However, using machine learning and diverse electronic health record (EHR) data for this task remains incompletely explored. We used EHR data from February-2017 to January-2020 from Oxfordshire, UK to predict hospital discharges in the next 24 h.
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Oct 10, 2024 |
nature.com | Karina-Doris Vihta |Koen B. Pouwels |Rebecca Guy |Katherine Henderson |Russell Hope |David A Clifton
AbstractPredicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April–March) for 22 pathogen–antibiotic combinations (FY2016-2017 to FY2021-2022).
Atrial fibrillation after cardiac surgery: identifying candidate predictors through a Delphi process
Sep 1, 2024 |
bmjopen.bmj.com | Jonathan Bedford |Kara Fields |Gary Collins |David A Clifton
DiscussionIn this consensus exercise, we aimed to identify important variables affecting AFACS risk. Highlighted variables may be used in the development of predictive models to inform future randomised trials or as covariates in prognostic studies. They will inform the development of two AFACS prediction models in the PARADISE project. Remote participation promoted international involvement.
Atrial fibrillation after cardiac surgery: identifying candidate predictors through a Delphi process
Sep 1, 2024 |
bmjopen.bmj.com | Jonathan Bedford |Kara Fields |Gary Collins |David A Clifton
DiscussionIn this consensus exercise, we aimed to identify important variables affecting AFACS risk. Highlighted variables may be used in the development of predictive models to inform future randomised trials or as covariates in prognostic studies. They will inform the development of two AFACS prediction models in the PARADISE project. Remote participation promoted international involvement.
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