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Dr Stephen Weng's SPCR fellowship research has found that computers that can teach themselves from routine clinical data are potentially better at predicting cardiovascular risk than current standard medical risk models.

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’. 

The University of Nottingham's Dr Stephen Weng said: "Cardiovascular disease is the leading cause of illness and death worldwide. Our study shows that artificial intelligence could significantly help in the fight against it by improving the number of patients accurately identified as being at high risk and allowing for early intervention by doctors to prevent serious events like cardiac arrest and stroke. 

“Current standard prediction models like the ACC are based on eight risk factors including age, cholesterol level and blood pressure but are too simplistic to account for other factors like medications, multiple disease conditions, and other non-traditional biomarkers. These AI algorithms have the potential to help save more lives”. 

Read the full press release from the University of Nottingham.

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