Using AI to Help Predict Cardiac Arrests

Perelman School of Medicine cardiologist Rajat Deo has been studying electrocardiographic (ECG) data and cardiac rhythms for nearly two decades at Penn.

He says that every second, hospitals generate “enormous streams of ECG data — electrical traces of the heart that accumulate into one of medicine’s richest, most underused archives.” For decades, most of this information had been treated as crucial in the moment, but it goes unused later.

Now, Deo and other clinicians from Penn Medicine have partnered with computer scientists at Penn’s School of Engineering and Applied Science to change that by harnessing the power of the cardiac data that hospitals already collect.

Cardiologists like Deo bring clinical insight into how small electrical irregularities can foreshadow serious cardiac events, while computer scientists like Rajeev Alur bring decades of work on systems that find patterns in complex, constantly evolving streams of information and use them to make predictions.

“At Penn, you can walk across the street and find a clinician who can challenge you to develop an AI-based solution to a problem that they want to solve,” Alur says.

Together with students and faculty across the University, the team developed the Cardiac Autoregressive Model for ECG Language-Modeling (CAMEL), an artificial intelligence model that treats ECG less like isolated snapshots and more like language.

Rather than simply identifying abnormalities after they appear, CAMEL analyzes longer stretches of heart rhythm to recognize patterns that may signal what comes next, paving the way for warnings of arrhythmias or cardiac arrest 10 to 15 minutes before they happen.

Read More at Penn Today

Pictured above, from left: Rajeev Alur, Mayank Keoliya (back row), Seewon Choi, Neelay Velingker, Sameed Khatana, Mayur Naik, Rajat Deo, Eric Wong, Cassandra Goldberg and Alireza Oraii. (Credit: Eric Sucar)