2024-10-23 エディンバラ大学
<関連情報>
- https://www.ed.ac.uk/news/2024/ai-tool-predicts-emergency-hospital-risk
- https://www.nature.com/articles/s41746-024-01250-1
スコットランドにおける緊急入院予測のための機械学習ツールの開発と評価 Development and assessment of a machine learning tool for predicting emergency admission in Scotland
James Liley,Gergo Bohner,Samuel R. Emerson,Bilal A. Mateen,Katie Borland,David Carr,Scott Heald,Samuel D. Oduro,Jill Ireland,Keith Moffat,Rachel Porteous,Stephen Riddell,Simon Rogers,Ioanna Thoma,Nathan Cunningham,Chris Holmes,Katrina Payne,Sebastian J. Vollmer,Catalina A. Vallejos & Louis J. M. Aslett
npj Digital Medicine Published:23 October 2024
DOI:https://doi.org/10.1038/s41746-024-01250-1
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.
