Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.

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Publication Details

Journal
npj Digital Medicine
Published
2024-10-23
DOI
https://doi.org/10.1038/s41746-024-01250-1
Citations
4
Primary Topic
Chronic Disease Management Strategies
Type
article
Field-Weighted Citation Impact
1.55

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article

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Sebastian J. Vollmer, Catalina A. Vallejos, David L. Carr, Louis J. M. Aslett et al.
4 citations
npj Digital Medicine
Chronic Disease Management Strategies
1.55
article

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Sebastian J. Vollmer, Catalina A. Vallejos, David L. Carr, Louis J. M. Aslett, Ioanna Thoma, Gergő Bohner, Stephen Riddell, Jill Ireland, Samuel D. Oduro, Keith Moffat, Simon Rogers, Katie Borland, Rachel Porteous, Samuel R. Emerson, Nathan Cunningham, James Liley, Bilal Akhter Mateen, Scott Heald, Chris Holmes, Katrina Payne
article en
4 citations

Abstract

Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.

npj Digital MedicineVol. 7(1)
Turing Institute (GB), National Health Service Scotland (GB), University of Kaiserslautern (DE), University of St Andrews (GB), Durham University (GB), University of Koblenz and Landau (DE), Edinburgh Cancer Research (GB), German Research Centre for Artificial Intelligence (DE), University of Warwick (GB), University of Oxford (GB), Public Health Scotland (GB), The Alan Turing Institute (GB), Institute of Genetics and Cancer (GB), University College London (GB), Wellcome Trust (GB), University of Edinburgh (GB)
Impact Fund, Wellcome Trust, NHS Health Scotland, Alan Turing Institute, UK Research and Innovation, Department of Health and Social Care, University of Warwick, King's College London, University of Edinburgh, Durham University, Medical Research Council, Engineering and Physical Sciences Research Council
Gender equality
Openalex Percentile: Top 16%
Chronic Disease Management Strategies
1.55
DOIOpen Access PDFOpenAlex
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