Artificial Intelligence Predicting Heart Attack
Medicine is one of the fields that AI promises to disrupt, and while doctors have lots of tools for predicting a patient’s health. even they will tell you, they’re no match for the complexity of the human body.
Heart attacks, in particular, are hard to anticipate. Now, scientists have shown that computers capable of teaching themselves can perform even better than standard medical guidelines, significantly increasing prediction rates.
If implemented, the new method could save thousands or even millions of lives a year.
“I can’t stress enough how important it is,” says Elsie Ross, a vascular surgeon at Stanford University in Palo Alto, California, who was not involved with the work, “and how much I really hope that doctors start to embrace the use of artificial intelligence to assist us in care of patients.”
Each year, nearly 20 million people die from the effects of cardiovascular disease, including heart attacks, strokes, blocked arteries, and other circulatory system malfunctions.
In an effort to predict these cases, many doctors use guidelines similar to those of the American College of Cardiology/American Heart Association (ACC/AHA).
Those are based on eight risk factors—including age, cholesterol level, and blood pressure—that physicians effectively add up.
Predicting heart attacks more accurately based on black-box algorithms are hard for doctors to trust.
As researchers teach algorithms to help diagnose real patients and once trained, the algorithm was able to flag still-living patients in a hospital’s system that might be good candidates for palliative care.
Big Data Meet AI
In the recently published study, the researchers unveiled an algorithm trained to analyze diagnoses, prescriptions, demographics, and other factors within electronic health records, during that 3 to 12 month period before a patient passed away.
When Stanford Hospital’s palliative care team assessed 50 randomly chosen patients that the algorithm had flagged as being very high risk, the team found that all of them were appropriate to be referred.
Because the algorithm doesn’t make any decisions about care that patients receive–instead, it simply highlights people that may have been overlooked–the researchers believe it could help doctors give this end-of-life care to more people who really need it.
“The human doctor is always in the loop, and the program is not an automated clinical decision system,” says Anand Avati, a PhD student who studies machine learning at Stanford. “The current flow of referrals that are initiated by the treatment team continues as is. The program identifies among the rest of the patients, proactively, those who might benefit from a consult and might have otherwise slipped through the cracks.”
Avati, Shah, and the other researchers recently started a pilot program at Stanford Hospital to test whether the algorithm could improve the statistics on how many patients get the end-of-life care they need.
Their research also reveals how AI might be used in medicine so that doctors aren’t forced to trust it. Instead, it presents a model for how algorithms could work side by side with doctors, augmenting their skills so that more people get the care they need. “The program only helps the doctor be more operationally efficient,” Avati says.