With the widespread deployment of near real time population health monitoring, there is increasing focus on spatial cluster detection for identifying disease outbreaks. These spatial epidemiologic methods rely on knowledge of patient location to detect unusual clusters. In hospital administrative data, patient location is collected as home address but use of this precise location raises privacy concerns. Regional locations, such as center points of zip codes, have been deployed in many existing systems.

Outbreaks of infectious diseases are identified in a variety of ways by clinicians and public health practitioners but not usually by analytic methods typically employed in syndromic surveillance. Systematic spatial-temporal analysis of statewide data may enable earlier detection of outbreaks and identification of multi-jurisdictional outbreaks.

Objective

Irregularly shaped spatial disease clusters occur commonly in epidemiological studies, but their geographic delineation is poorly defined. Most current spatial scan software usually displays only one of the many possible cluster solutions with different shapes, from the most compact round cluster to the most irregularly shaped one, corresponding to varying degrees of penalization parameters imposed to the freedom of shape.

Chief complaints are often represented textually and as a mixture of complex and context-dependant lexical symbols with little formal sentence structure. Although human experts usually comprehend this information in its right context intuitively and effortlessly, use of chief complaint data by computers is a challenge. Semantic approaches for text understanding are concerned with the meaning of terms and their relationships, driven from an explicit model rather than their syntactic forms.