Heart-Risk Model Saves Lives Deep learning model identifies high-risk patients from EKG readings

May 29, 2024
Reading time
2 min read
Heart-Risk Model Saves Lives: Deep learning model identifies high-risk patients from EKG readings

A deep learning model significantly reduced deaths among critically ill hospital patients.

What’s new: A system built by Chin-Sheng Lin and colleagues at Taiwan’s National Defense Medical Center analyzed patients’ heart signals and alerted physicians if it detected a high risk of death. It reduced deaths of high-risk patients by 31 percent in a randomized clinical trial.

How it works: Researchers trained a convolutional neural network, given an electrocardiogram (a measurement of the heart’s electrical activity), to estimate a risk score. The system compares a patient’s risk score against those of other patients. Scores that rank in the 95th percentile or higher are considered high risk of death within 90 days.

  • The authors tested the system on 16,000 patients at two hospitals for 90 days.
  • Patients in the experimental group were measured by electrocardiograms, which were fed to the system. If the system identified a high-risk patient, it alerted their attending physician.
  • The control group received typical care. The model monitored their electrocardiograms, but physicians saw its output only after the trial was over. 

Results: 8.6 percent of patients in the control group and 8.9 percent of patients in the experimental group raised a high-risk alert during the trial. In the experimental group, 16 percent of high-risk patients died; in the control group, 23 percent of high-risk patients died. Overall, in the experimental group, 3.6 percent of patients died; in the control group, 4.3 percent of patients died. The model was trained to predict mortality from all causes, but it showed unusually strong predictive capability for heart-related deaths. Examining causes of death, the authors found that 0.2 percent of patients in the experimental group died from heart-related conditions such as cardiac arrest versus 2.4 percent in the control group.

Behind the news: Hospitals use AI-powered alert systems to identify patients in need of urgent medical attention. Such systems monitor emergency room patients for sepsis, predict whether those patients need intensive care, and predict the risk that discharged patients will require further care. They help hospitals to allocate resources by directing attention where it’s needed most urgently.

Why it matters: It’s rare for any kind of medical intervention to reduce mortality in a subgroup by 31 percent. The authors speculate that the system not only helped direct attention to patients urgently in need of attention but also may have identified electrocardiogram features that doctors typically either don’t understand well or can’t detect.

We’re thinking: This relatively low-cost AI system unambiguously saved lives over three months at different hospitals! We look forward to seeing it scale up.


Subscribe to The Batch

Stay updated with weekly AI News and Insights delivered to your inbox