LONDON, Sept 01 (BBC): Artificial intelligence (AI) could help GPs identify patients most at risk of developing conditions that could lead to fatal heart problems.
The University of Leeds has helped train an AI system called Optimise, that looked at health records of more than two million people. Researchers found that in many cases patients had undiagnosed conditions, or had not received the medications that could help reduce their risk.
Dr Ramesh Nadarajah, from the university, said preventing conditions worsening was often cheaper than treatment. Of those two million records that were scanned, more than 400,000 people were identified as being high risk for the likes of heart failure, stroke and diabetes.
This group made up 74% of patients who died of a heart-related condition. In an Optimise pilot involving 82 high-risk patients, one in five were found to have undiagnosed moderate or high-risk chronic kidney disease.
More than half of patients with high blood pressure were given different medication to better manage their heart risk. The approach could allow medics to treat patients earlier, helping to relieve pressures on the NHS, the study found.
Dr Nadarajah, a health data research fellow, said heart-related deaths are often caused by a constellation of factors. "This AI uses readily available data to gather new insights that could help healthcare professionals ensure that they are providing timely care for their patients."
Plans are in place to carry out a larger clinical trial, according to researchers, who presented their findings at the European Society of Cardiology Congress in London.
"We hope our research will ultimately benefit patients living with heart and circulatory diseases, as well as helping relieve pressure off our NHS systems," Dr Nadarajah added.
"Next, we plan to perform a clinical trial where we are providing doctor-led care to patients."
Prof Bryan Williams, chief scientific and medical officer at the British Heart Foundation, which funded the study, said diagnosing people early was key to reducing hospital admissions.
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