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

• URL: http://arxiv.org/abs/2110.13683v1
• Date: Tue, 26 Oct 2021 13:19:28 GMT
• Title: BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network
• Authors: Jialun Wu, Yang Liu, Zeyu Gao, Tieliang Gong, Chunbao Wang and Chen Li
• Abstract summary: We propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports. 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.
• Score: 9.227487525657901
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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