The model was designed using the electronic health data of over 80,000 patients, collected as part of routine care.
Scientists, including those from the University College London in the UK, wanted to see if they could create a model for coronary artery disease -- the leading cause of death in the UK -- that outperforms experts using self-taught machine learning techniques.
Coronary artery disease develops when the major blood vessels that supply the heart with blood, oxygen and nutrients become damaged or narrowed by fatty deposits.
Eventually, restricted blood flow to the heart can lead to chest pain and shortness of breath, while a complete blockage can cause a heart attack.
An expert-constructed prognostic model for coronary artery disease which this work was compared against made predictions based on 27 variables chosen by medical experts, such as age, gender and chest pains.
By contrast, the AI algorithms to train themselves, searching for patterns and picking the most relevant variables from a set of 600.
Not only did the new data-driven model beat expert-designed models at predicting patient mortality, but it also identified new variables that doctors hadn't thought of.
"Along with factors like age and whether or not a patient smoked, our models pulled out a home visit from their GP as a good predictor of patient mortality," said Steele.
"Home visits are not something a cardiologist might say is important in the biology of heart disease, but perhaps a good indication that the patient is too unwell to make it to the doctor themselves, and a useful variable to help the model make accurate predictions," he said.