An interdisciplinary team convened by ISDS to translate public health use-case needs into well-defined technical problems recently identified the need for new pre-syndromic surveillance methods that do not rely on existing syndromes or pre-defined illness categories1. Our group has recently developed Multidimensional Semantic Scan (MUSES), a pre-syndromic surveillance approach that (1) uses topic modeling to identify newly emerging syndromes that correspond to rare or novel diseases; and (2) uses multidimensional scan statistics to identify emerging outbreaks that correspond to these syndromes and are localized to a particular geography and/or subpopulation2,3. Through a blinded evaluation on retrospective free-text ED chief complaint data from NYC DOHMH, we demonstrate that MUSES has great potential to serve as a safety net for public health surveillance, facilitating a rapid, targeted, and effective response to emerging novel disease outbreaks and other events of relevance to public health that do not fit existing syndromes and might otherwise go undetected.
Objective: We present a new approach for pre-syndromic disease surveillance from free-text emergency department (ED) chief complaints, and evaluate the method using historical ED data from New York City's Department of Health and Mental Hygiene (NYC DOHMH).