To get the model up and running, the team first separated each patient's signal into a collection of adjacent heart beats. They then assigned a label - i.e. whether or not the patient died of cardiovascular death - to each set of adjacent heartbeats. The researchers trained the model to classify each pair of adjacent heartbeats to its patient outcome: heartbeats from patients who died were labeled "risky," while heartbeats from patients who survived were labeled "normal."
Given a new patient, the team created a risk score by averaging the patient prediction from each set of adjacent heartbeats.
Within the first 15 minutes of a patient experiencing an ACS, there was enough information to estimate whether or not they would suffer from cardiovascular death within 30, 60, 90 or 365 days.
Still, calculating a risk score from just the ECG signal is no simple task. The signals are very long, and as the number of inputs to a model increase, it becomes harder to learn the relationship between those inputs.
The team tested the model by producing risk scores for a set of patients. Then, they measured how much more likely a patient would suffer from cardiovascular death as a high-risk patient when compared to a set of low-risk patients. They found that in roughly 1250 post-ACS patients, 28 would die of cardiovascular death within a year. Using the proposed risk score, 19 of those 28 patients were classified as high-risk.
In the future, the team hopes to make the dataset more inclusive to account for different ages, ethnicities, and genders. They also plan to examine medical scenarios where there's a lot of poorly labeled or unlabeled data, and evaluate how their system processes and handles that information to account for more ambiguous cases.
"Machine learning is particularly good at identifying patterns, which is deeply relevant to assessing patient risk,'' says Shanmugam. "Risk scores are useful for communicating patient state, which is valuable in making efficient care decisions."
Shanmugam presented the paper at the Machine Learning for Healthcare Conference alongside PhD student Davis Blalock and MIT professor John Guttag.
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