Evolutionary Computation. Evolutionary computation is a fascinating subfield of artificial intelligence and soft computing that draws inspiration from biological evolution to solve complex optimization problems. Here’s a deeper dive into its key aspects: Core Concepts Population-Based Approach : Evolutionary computation involves a population of potential solutions to a given problem. These solutions evolve over time through processes analogous to natural selection and genetic variation. Fitness Evaluation : Each candidate solution is evaluated based on a fitness function, which measures how well it solves the problem at hand. The better the solution, the higher its fitness score. Selection : Solutions with higher fitness scores are more likely to be selected for reproduction. This mimics the natural selection process where the fittest individuals are more likely to pass on their genes.
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."
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