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

[...] 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.'

Original Submission