Doctors use risk scores to make treatment decisions, but these scores are based on just a handful of variables and often have modest accuracy in individual patients.
Through repetition and adjustment, machine learning, the bedrock of AI, could exploit large amounts of data and identify complex patterns that might not be evident to humans, said the study presented at the International Conference on Nuclear Cardiology and Cardiac CT (ICNC) 2019 in Portugal.
"Doctors already collect a lot of information about patients, for example those with chest pain. We found that machine learning can integrate these data and accurately predict individual risk. This should allow us to personalise treatment and ultimately lead to better outcomes for patients," said study author Luis Eduardo Juarez-Orozco of the Turku PET Centre in Finland.
A machine learning algorithm called LogitBoost repeatedly analysed 85 variables in the 950 patients with known six-year outcomes.
"The algorithm progressively learns from the data and after numerous rounds of analyses, it figures out the high dimensional patterns that should be used to efficiently identify patients who have the event. The result is a score of individual risk," Juarez-Orozco said.
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