Machine Learning Models Help Predict Which Patients Need A&E

Machine learning a field of artificial intelligence that uses statistical techniques to enable computer systems to ‘learn’ from data can be used to analyze electronic health records and predict the risk of emergency hospital admissions, a new study from The George Institute for Global Health at the University of Oxford has found. The research, published in the journal PLOS Medicine, suggests that using these techniques could help health practitioners accurately monitor the risks faced by patients and put in place measures to avoid unplanned admissions, which are a major source of healthcare spending.
‘There were over 5.9 million recorded emergency hospital admissions in the UK in 2017, and a large proportion of them were avoidable. We wanted to provide a tool that would enable healthcare workers to accurately monitor the risks faced by their patients, and as a result make better decisions around patient screening and proactive care that could help reduce the burden of emergency admissions,’ said Fatemeh Rahimian, former data scientist at The George Institute UK, who led the research.