Unified Approach to Retrospective Event Detection for Event Based Epidemic Intelligence
Abstract: Inferring the magnitude and occurrence of real-world events from natural language text is a crucial task in various domains. Particularly in the domain of public health, the state-of-the-art document and to- ken centric event detection approaches have not kept the pace with the growing need for more robust event detection in public health. In this paper, we propose UPHED, a unified approach, which combines both the document and token centric event detection techniques in an unsupervised manner such that events which are: rare (aperiodic); reoccurring (periodic) can be detected using a generative model for the domain of public health. We evaluate the efficiency of our approach as well as its effectiveness for two real-world case studies with respect to the quality of document clusters. Our results show that we are able to achieve a precision of 60% and a recall of 71% analyzed using manually annotated real-world data. Finally, we also make a comparative analysis of our work with the well established rule-based system of MedISys and nd that UPHED can be used in a complementary way to not only detect similar anomalies as MedISys, but can also deliver more information about the specific outbreak of reported diseases.
#Dataset we collected source documents (articles) from MediSys feed. The data were gathered for a two months period, from May 1 to June 30, 2011. In total 13; 076 documents were collected.