AI’s ability to predict threats to your health could soon include deadly heart conditions. Researchers at MIT’s CSAIL have developed a machine learning system, RiskCardio, that can estimate the risk of death due to cardiovascular issues that block or reduce blood flow. All it needs is a 15-minute ECG reading — from there, it gauges the danger based on the sets of consecutive beats in the sample. If the data is captured within 15 minutes of an event, RiskCardio can determine whether or not someone will die within 30 days, or even up to a year later.
The approach is based on the notion that greater variability between heartbeats reflects greater risk. Scientists trained the machine learning system using historical data for patient outcomes. If a patient survived, their heartbeats were deemed relatively normal; if a patient died, their heart activity was considered risky. The ultimate risk score comes by averaging the prediction from each set of consecutive heartbeats.
There’s plenty of work to be done, including refining the training data to account for more ages, ethnic backgrounds and genders. It clearly needs to be accurate when mistakes could have dangerous consequences. If RiskCardio does enter service, though, it could prove vital to health care. Doctors could quickly assess a patient’s health and decide on an appropriate level of treatment. CSAIL also hopes it can help understand less-than-clear scenarios by running poorly-labeled data through the system.