[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126917-en":3,"doc-seo-126917-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126917,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",7,"Healthcare","Development and assessment of a machine learning tool for predicting emergency admission in Scotland","Emergency admissions (EA) requiring urgent in-hospital care pose major challenges for healthcare systems. The document presents SPARRAv4, a predictive risk score for EA in Scotland designed for nationwide deployment. SPARRAv4 is derived from supervised and unsupervised machine-learning on routinely collected electronic health records of about 4.8M residents (2013–2018). Results show improved discrimination and calibration versus prior Scottish scores, plus stability across three years.","Edinburgh Research Explorer  \nDevelopment and assessment of a machine learning tool for predicting emergency admission in Scotland  \nCitation for published version:  \nLiley, J, Bohner, G, Emerson, SR, Mateen, BA, Borland, K, Carr, D, Heald, S, Oduro, SD, Ireland, J, Moffat, K, Porteous, R, Riddell, S, Rogers, S, Thoma, I, Cunningham, N, Holmes, C, Payne, K, Vollmer, SJ, Vallejos, CA & Aslett, LJM 2024, 'Development and assessment of a machine learning tool for predicting emergency admission in Scotland', npj Digital Medicine. [https://doi.org/10.1038/s41746-024-01250-1](https://doi.org/10.1038/s41746-024-01250-1)  \nDigital Object Identifier (DOI):  \n10.1038/s41746-024-01250-1  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPeer reviewed version  \nPublished In:  \nnpj Digital Medicine  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 25. Nov. 2025  \n1 Development and assessment of a machine learning tool  \n2 for predicting emergency admission in Scotland  \n3 James Liley 1,2,3,?,*, Gergo Bohner 1,4,?, Samuel R. Emerson3 ,  \n4 Bilal A. Mateen 1,5 , Katie Borland6 , David Carr6 , Scott Heald6 , 5 Samuel D. Oduro6b , Jill Ireland6 , Keith Mo􀀋at6,7 , Rachel Porteous6 , Stephen  \n6 Riddell6b , Simon Rogers8 , Ioanna Thoma 1,2 , Nathan Cunningham 1,9 , Chris  \n7 Holmes 1,10 , Katrina Payne 1 , Sebastian J. Vollmer 1,4 ,  \n8 Catalina A. Vallejos 1,2,*,y, and Louis J. M. Aslett 1,3,*,y  \n9 1 Alan Turing Institute, London, UK  \n10 2 MRC Human Genetics Unit, Institute of Genetics and Cancer, University of  \n11 Edinburgh, UK  \n12 3 Department of Mathematical Sciences, Durham University, UK  \n13 4 Mathematics Institute, University of Warwick, UK  \n14 5 Institute of Health Informatics, University College London, UK, and  \n15 Wellcome Trust, London, UK  \n16 6 Public Health Scotland (PHS) . (b): former employee  \n17 7 University of St Andrews, UK  \n18 8 NHS National Services Scotland, UK  \n19 9 Department of Statistics, University of Warwick, UK  \n20 10 Department of Statistics, University of Oxford, UK  \n21 ? Equal contribution  \n22 yEqual supervision  \n23 * Corresponding 24  \n25 JL: [james.liley@durham.ac.uk](james.liley@durham.ac.uk)  \n26 CAV: [catalina.vallejos@ed.ac.uk](catalina.vallejos@ed.ac.uk)  \n27 LJMA: [louis.aslett@durham.ac.uk](louis.aslett@durham.ac.uk)  \n28 September 2, 2024  \n29 Abstract  \n30 Emergency admissions (EA), where a patient requires urgent in-hospital care, are  \n31 a major challenge for healthcare systems. The development of risk prediction models  \n32 can partly alleviate this problem by supporting primary care interventions and public  \n33 health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that  \n34 will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and  \n35 unsupervised machine-learning methods applied to routinely collected electronic health  \n36 records from approximately 4.8M Scottish residents (2013-18) . We demonstrate im- 37 provements in discrimination and calibration with respect to previous scores deployed  \n38 in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides  \n39 insights about the epidemiology of EA risk in Scotland, by studying predictive perfor- 40 mance across di􀀋erent population sub-groups and reasons for admissio","cbCaitQHMulhqxLO","https://ap.wps.com/l/cbCaitQHMulhqxLO","pdf",504170,2,1,31,"English","en",105,"# Abstract\n# Introduction\n## Emergency admissions and healthcare challenge\n## Predictive risk models and potential interventions\n## SPARRA and motivation for local model development","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses emergency admissions in which patients need urgent in-hospital care, a major challenge for healthcare systems.\"},{\"question\":\"What is SPARRAv4 and what data was used to build it?\",\"answer\":\"SPARRAv4 is a predictive score for EA risk in Scotland, derived using machine-learning methods on routinely collected electronic health records from about 4.8M residents (2013–2018).\"},{\"question\":\"How does SPARRAv4 perform compared with earlier tools?\",\"answer\":\"The analysis demonstrates improvements in discrimination and calibration relative to previous deployed Scotland scores, and stability over a three-year timeframe.\"}]","Development and assessment of a machine learning tool for predicting emergency admission in Scotland | 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