Researchers Develop Machine-Learning System to Detect Public Health Threats

Researchers have recently developed an automated machine-learning system designed to detect rare or previously unseen disease clusters. Current automated systems used to identify public health threats rely on “syndromic surveillance” to detect existing threats but can fall short of identifying new ones. To close this surveillance gap, the researchers designed a system that would enable public health officials to respond more quickly and effectively to emerging unusual or novel threats. To develop the system, known as Multidimensional Semantic Scan (MUSES), the research team relied on a “pre-syndromic” surveillance approach, which leverages digitally communicated data on patient conditions. Learn more about the researched from HealthITAnalytics here.