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“It won’t be long before doctors are routinely using these sorts of tools in the clinic to make better diagnoses and prognoses, which can help them decide the best ways to care for their patients,” said Andrew Steele, from the Francis Crick Institute in the UK.
“Doctors already use computer-based tools to work out whether a patient is at risk of heart disease, and machine-learning will allow more accurate models to be developed for a wider range of conditions,” said Steele.
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.
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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.