END-TO-END RELATION EXTRACTION ON CLINICAL TEXT DATA USING NATURAL LANGUAGE PROCESSING

Abstract

In light of the increasing number of clinical narratives, a modern framework for assessing patient histories and carrying out clinical research has been developed. As a consequence of using existing approaches, the process for recognizing clinical entities and extracting relations from clinical narratives was subsequently error propagated. Thus, we propose an end-to-end clinical relation extraction model in this paper. Clinical XLNet has been used as the base model to address the discrepancy issue, and the proposed work has been tested with the N2C2 corpus.