TransformDDI: The Transformer-Based Joint Multi-Task Model for End-to-End Drug-Drug Interaction Extraction

Drug-Drug Interactions (DDI) identification is a part of the drug safety process, that focuses at avoiding potential adverse drug effects that can lead to patient health risks. With the exponential growth in published literature, it becomes increasingly difficult to extract useful information from the most relevant research. Therefore, Machine Learning and specifically Relationship Extraction has been employed for the extraction of DDIs from biomedical literature. This task consists of both Named Entity Recognition and Relationship Classification techniques tackled in either pipelined or joint approaches for recognizing drug mentions and classifying their interactions, respectively. However, current approaches are prone to error propagation between the tasks, not taking the relevance between them into account. In this paper we propose TransformDDI, an end-to-end Transformer-based joint multi-task DDI extraction model that integrates domain knowledge and a shared parameter layer in a dynamic drug entities extraction and interaction classification Language Model architecture. Our proposed model can generate variable outputs based on the recognized drug entities in a single-model architecture by implementing a Dynamic Pair Attention Mechanism with task-specific focus and dynamic loss functions. Experiments conducted on the DDI Extraction 2013 benchmark corpus indicate that our methodology offers significant improvements over the current state-of-the-art.