A recent study presented at the International Conference on Nuclear Cardiology and Cardiac CT (ICNC) shows machine learning gaining an edge over humans in predicting death or heart attack.
According to a news release by the European Society of Cardiology (ESC), an algorithm repeatedly analyzed 85 variables in 950 patients with known six-year outcomes and “learned” how imaging data interacts.
Ten clinical variables were obtained from medical records including sex, age, smoking and diabetes. A coronary computed tomography angiography (CCTA) scan produced 58 pieces of data on coronary plaque, vessel narrowing, and calcification. A positron emission tomography (PET) scan produced 17 variables on blood flow.
The algorithm was then capable of detecting patterns correlating the variables to death and heart attack with an accurate rate of over 90%.
Study author Dr Luis Eduardo Juarez-Orozco, of the Turku PET Centre, Finland, said: “These advances are far beyond what has been done in medicine, where we need to be cautious about how we evaluate risk and outcomes. We have the data but we are not using it to its full potential yet.”
When doctors make treatment decisions they use risk scores based on just a handful of variables. There is a lot of potential for machine learning to exploit massive quantities of data and identify complex patterns may not have been detected otherwise.
Dr Juarez-Orozco explained: “Humans have a very hard time thinking further than three dimensions (a cube) or four dimensions (a cube through time). The moment we jump into the fifth dimension we’re lost. Our study shows that very high dimensional patterns are more useful than single dimensional patterns to predict outcomes in individuals and for that we need machine learning.”
Dr Juarez-Orozco said: “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.”
He added: “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.”