Masking Language Model Mechanism with Event-Driven Knowledge Graphs for Temporal Relations Extraction from Clinical Narratives

For many natural language processing systems, the extraction of temporal links and associations from clinical narratives has been a critical challenge. To understand such processes, we must be aware of the occurrences of events and their time or temporal aspect by constructing a chronology for the sequence of events. The primary objective of temporal relation extraction is to identify relationships and correlations between entities, events, and expressions. We propose a novel architecture leveraging Transformer based graph neural network by combining textual data with event graph embeddings for predicting temporal links across events, entities, document creation time and expressions. We demonstrate our preliminary findings on i2b2 temporal relations corpus for predicting BEFORE, AFTER and OVERLAP links with event graph for correct set of relations. Comparison with various Biomedical-BERT embedding types were benchmarked yielding best performance on PubMed BERT with language model masking (LMM) mechanism on our methodology. This illustrates the effectiveness of our proposed strategy.