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Machine learning can be used to predict which patients require emergency admission

Machine learning can help healthcare workers predict whether patients may require emergency hospital admission, new study finds

University of Oxford

Machine learning - a field of artificial intelligence that uses statistical techniques to enable computer systems to 'learn' from data - can be used to analyse 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,' said Fatemeh Rahimian, former data scientist at The George Institute UK, who led the research.

'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.'

The study, of 4.6 million patients from 1985 to 2015, was conducted using linked electronic health records from the UK's Clinical Practice Research Datalink. A wide range of factors was taken into account, including age, sex, ethnicity, socioeconomic status, family history, lifestyle factors, comorbidities, medication and marital status, as well as the time since first diagnosis, last use of the health system and latest laboratory tests.

Using more variables combined with information about their timing, machine learning models were found to provide a more robust prediction of the risk of emergency hospital admission than any models used previously.

'Our findings show that with large datasets which contain rich information about individuals, machine learning models outperform one of the best conventional statistical models,' Rahimian said. 'We think this is because machine learning models automatically capture and 'learn' from interactions between the data that we were not previously aware of.'

Whether machine learning models can lead to similarly strong improvements in risk prediction in other areas of medicine requires further research.

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The study by The George Institute UK was funded by the Oxford Martin School's Deep Medicine programme.

The paper in PLOS Medicine will be available at this link once the embargo lifts: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002695

Media enquiries

Ana Bow-Bertrand
Communications Manager
The George Institute for Global Health, University of Oxford
Tel: 07918 553680
anastasia.bow-bertrand@georgeinstitute.ox.ac.uk

Emma Feeny
Senior Advocacy Advisor
The George Institute for Global Health, University of Oxford
Tel: 07864 652347 emma.feeny@georgeinstitute.ox.ac.uk

About The George Institute for Global Health

The George Institute for Global Health conducts clinical, population and health system research aimed at changing health practice and policy worldwide. The Institute has a global network of medical and health experts working together to address the leading causes of death and disability. Established in Australia and affiliated with UNSW Sydney, the Institute today also has offices in China, India and the United Kingdom. The George Institute UK was established in 2010 in partnership with the University of Oxford. Facebook at thegeorgeinstitute Twitter @GeorgeInstUK Web georgeinstitute.org.uk

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