Using machine learning to estimate risk of cardiovascular death

Using machine learning to estimate risk of cardiovascular death

Press releases may be edited for formatting or style | September 13, 2019 Artificial Intelligence Cardiology
Humans are inherently risk-averse: we spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair.

Still, our measures for controlling the inner-workings of our biology can be a little more unruly.

With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient's risk of cardiovascular death.

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The system, called "RiskCardio," focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there's a reduction or blockage of blood to the heart. Using just the first fifteen minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.

RiskCardio's high-risk patients - patients in the top quartile - were nearly seven times more likely to die of cardiovascular death when compared to the low-risk group in the bottom quartile. By comparison, patients identified as high risk by the most common existing risk metrics were only three times more likely to suffer an adverse event compared to their low-risk counterparts.

"We're looking at the data problem of how we can incorporate very long time series into risk scores, and the clinical problem of how we can help doctors identify patients at high risk after an acute coronary event," says Divya Shanmugam, lead author on a new paper about RiskCardio. "The intersection of machine learning and healthcare is replete with combinations like this -- a compelling computer science problem with potential real-world impact."

Risky Business

Previous machine learning models have attempted to get a handle on risk by either making use of external patient information like age or weight, or using knowledge and expertise specific to the system - (more broadly known as domain-specific knowledge) - to help their model select different features.

RiskCardio, however, uses just the patients' raw ECG signal -- with no additional information.

Say a patient checks into the hospital following an ACS. After intake, a physician would first estimate risk of cardiovascular death or heart attack using medical data and lengthy tests, and then choose a course of treatment.

RiskCardio aims to improve that first step of estimating risk. To do this, the system separates a patient's signal into sets of consecutive beats, with the idea that variability between adjacent beats is telling of downstream risk. The system was trained using data from a study of past patients.

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