Computers that can teach themselves from routine clinical data are potentially better at predicting cardiovascular risk than current standard medical risk models, according to new research at the University of Nottingham.
The team of primary care researchers and computer scientists compared a set of standard guidelines from the American College of Cardiology (ACC) with four ‘machine-learning’ algorithms – these analyse large amounts of data and self-learn patterns within the data to make predictions on future events – in this case, a patient’s future risk having of heart disease or a stroke.
The results, published in the online journal PLOS ONE, showed that the self-teaching ‘artificially intelligent’ tools were significantly more accurate in predicting cardiovascular disease than the established algorithm. In computer science, the AI algorithms that were used are called ‘random forest’, ‘logistic regression’, ‘gradient boosting’ and ‘neural networks’.
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Posted on Monday 24th April 2017