Event-Based Surveillance Systems (EBS) are crucial for detecting emerging public health threats. However, these systems face significant challenges, including overreliance on manual expert intervention, limited handling of heterogeneous textual data, etc. The Description-Detection Framework (DDF) addresses some of these limitations by leveraging PropaPhen (Core Propagation Phenomenon Ontology), UMLS, and OpenStreetMaps to detect suspicious health-related cases using spatiotemporal and textual data. However, DDF is restricted to detection and lacks the ability to classify the detected observations into meaningful categories.

To adress this limitation, we propose to enhance DDF by incorporating a clustering-based classification process. This enhancement employs BioSTransformers, a pretrained biomedical language model built on Sentence Transformers trained on PubMed data, to compute semantic similarity between observations. By capturing domain-specific semantic relationships, BioSTransformers enables clustering that integrates biological semantics with spatiotemporal context, outperforming traditional methods from the literature in observation classification. Our proposed approach reduces the dependency on manual expert effort, improves the system's ability to process heterogeneous data, and enhances the accuracy and contextual relevance of case classification. The results demonstrate the potential of this method to advance EBS systems, providing a scalable and automated solution to public health surveillance challenges.