Researchers help AI express uncertainty to improve health monitoring tech

A team of engineering and health researchers has developed a tool that improves the ability of electronic devices to detect when a human patient is coughing, which has applications in health monitoring. The new tool relies on an advanced artificial intelligence (AI) algorithm that helps the AI better identify uncertainty when faced with unexpected data in real-world situations. The paper, "Robust Cough Detection with Out-of-Distribution Detection," is published in the IEEE Journal of Biomedical and Health Informatics. "When AI is being trained to identify the sound of coughing, this is usually done with'clean' data--there is not a lot of background noise or confusing sounds," says Edgar Lobaton, corresponding author of a paper on the work and an associate professor of electrical and computer engineering at North Carolina State University. "But the real world is full of background noise and confusing sounds. So previous cough detection technologies often struggled with'false positives'--they would say that someone was coughing even if nobody was coughing. "We've developed an algorithm that helps us address this problem by allowing an AI to express uncertainty.