End-to-end drug entity recognition and adverse effect relation extraction via principal neighbourhood aggregation network
Luyue Kong, Qinghan Lai, Song Liu · Journal of Physics Conference Series · 2021
Abstract Drug entity and adverse effect relation extraction is a critical task that aims to recognize drug entity and extract adverse effect relation from the unstructured medical text. Many works have used statistical learning approach, traditional or new deep learning approach to solve similar pharmacovigilance problem. Recent works tended to employ the graph convolutional network to enhance the ability of drug and adverse effect information extraction. However, the injective problem generated by a single aggregator and the weak generalization of the summation aggregator cause the graph neural network lack the ability to extract sufficient relation information. To solve these problems, we propose a new end-to-end model named recognizing Drug entities and extracting Adverse effect relations via the Principal Neighbourhood Aggregation network (DAPNA). Moreover, we compared the DAPNA with baseline models on the Adverse Drug Effect (ADE) dataset using multiple metrics. The experimental results demonstrate the method proposed in this paper achieves state-of-the-art results and can be applied to other drug and adverse effect information extraction tasks.