New research has found that machine learning can help healthcare workers predict whether patients may require emergency hospital admission.

The research, from The George Institute for Global Health at the University of Oxford, was first published in the journal PLOS Medicine. It was led by The George Institute UK former data scientist Fatemeh Rahimian.

The study suggests that machine learning, a field of artificial intelligence (AI) that enables computer systems to learn from data utilising statistical techniques, can be used to assess electronic health records of a person to predict emergency hospital admission risks.

“Once used, the machine learning process can help in avoiding unplanned admissions.”

The usage of the new technology can help medical practitioners to analyse health risks faced by patients and determine the need of hospital admission.

Once used, the machine learning process can help in avoiding unplanned admissions, the report added.

Rahimian said: “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.”

Overall, the study assessed records of 4.6 million patients from 1985 to 2015 using linked electronic health records from the UK’s Clinical Practice Research Datalink.

The study encompassed various factors including age, sex, ethnicity, lifestyle factors socioeconomic status, family history, medication status and latest laboratory tests among others.

Using more variables and information about their timing, the machine learning models were found to offer a more accurate prediction of the risk of emergency hospital admission, the study found.

Rahimian added: “Our findings show that with large datasets which contain rich information about individuals, machine learning models outperform one of the best conventional statistical models.

“We think this is because machine learning models automatically capture and ‘learn’ from interactions between the data that we were not previously aware of.”