BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network

TL;DR

This paper introduces BioIE, a hybrid neural network leveraging multi-head attention and graph convolutional networks to improve biomedical relation extraction from noisy medical reports, aiding large-scale knowledge graph construction.

Contribution

It proposes a novel hybrid neural network model that effectively captures complex relations and context in biomedical texts, outperforming existing methods and demonstrating transfer learning capabilities.

Findings

Achieves superior performance on biomedical relation extraction tasks.

Effectively resists noise in biomedical text data.

Shows promising transfer learning results across different medical report formats.

Abstract

Constructing large-scaled medical knowledge graphs can significantly boost healthcare applications for medical surveillance, bring much attention from recent research. An essential step in constructing large-scale MKG is extracting information from medical reports. Recently, information extraction techniques have been proposed and show promising performance in biomedical information extraction. However, these methods only consider limited types of entity and relation due to the noisy biomedical text data with complex entity correlations. Thus, they fail to provide enough information for constructing MKGs and restrict the downstream applications. To address this issue, we propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports. Our model utilizes a multi-head attention enhanced graph convolutional network to capture the complex relations and context information while resisting the noise from the data. We evaluate our model on two major biomedical relationship extraction tasks, chemical-disease relation and chemical-protein interaction, and a cross-hospital pan-cancer pathology report corpus. The results show that our method achieves superior performance than baselines. Furthermore, we evaluate the applicability of our method under a transfer learning setting and show that BioIE achieves promising performance in processing medical text from different formats and writing styles.