Machine learning could warn us about the next public health threat

You are free to share this article under the Attribution 4.0 International license. Researchers have created a new approach to detecting public health threats. A dire threat to public health can emerge from a huge variety of sources--for example, infectious diseases, a spate of drug overdoses, or exposures to toxic chemicals. Federal, state, and local health departments must respond rapidly to disease outbreaks and other emerging bio-threats. While the current automated systems for "syndromic surveillance" can help by monitoring health data and detecting disease clusters, they are not able to detect clusters with rare or previously unseen symptomology. The method is incorporated in an automated system that can enable public health practitioners to respond more quickly and effectively in the future to fast-emerging threats, including those that are unusual or novel.