Case Classification Proof of Concept

State of the art analytics for Public Health community will improve data quality and understanding. A proof of concept demonstrating automated reasoning services at the CDC is presented. These services provide the knowledge base rule development for automating case classification, event detection, and decision support.. This work provides the basis for a wide variety of public health applications, including message content validation, content classification analysis, and automated event detection in support of the broader public health mission. The presentation provides an overview of the foundation technologies, public health knowledge bases, and business drivers behind the implementation of this proof of concept capability. Reasoners for case classification, event detection, and message validation have potential for providing support to a variety public health mission requirements. This includes surveillance report content validation, BioSense message classification, and individual program alerting functions such as drug resistant TB. Specific topics that will be covered during the presentation will include: the standardized domain knowledge representation that has been developed in a consistent and highly constrained ontology; generalized classification rule-based implementation that can be applied to the domain ontology built with a specific set of knowledge base conditions and HL7 message sources; and decision support rules that provide analytical support for correlating classification results based upon specific application requirements. To demonstrate possible implementation strategies, the presentation will cover how this technology is integrated in a service-based environment within the Data Message Broker environment and can be tailored to a variety of programs, target deployment environments, and maintenance schemes.