论文标题
疾病关系提取图的多模式学习
Multimodal Learning on Graphs for Disease Relation Extraction
论文作者
论文摘要
目的：疾病知识图是一种联系，组织和访问有关疾病的不同信息，对人工智能（AI）有许多好处。为了创建知识图，有必要以疾病概念之间关系的形式从多模式数据集中提取知识，并将概念和关系类型归一化。 方法：我们引入了Remap，这是一种用于疾病关系提取和分类的多模式方法。重新启动机器学习方法将部分不完整的知识图和医学语言数据集嵌入紧凑的潜在矢量空间中，然后将多模式嵌入以进行最佳疾病关系提取。 结果：我们将重新映射方法应用于具有96,913个关系的疾病知识图和124万句的文本数据集。在由人类专家注释的数据集中，Remap通过将疾病知识图与文本信息融合，将基于文本的疾病关系提取提高了10.0％（准确性）和17.2％（F1分数）。此外，重建利用文本信息在知识图中推荐新的关系，优于基于图的方法，高于8.4％（准确性）和10.4％（F1得分）。 结论：重现是通过融合结构化知识和文本信息来提取和分类疾病关系的多模式方法。 Remap提供了灵活的神经结构，可轻松找到，访问和验证疾病概念之间的AI驱动关系。
Objective: Disease knowledge graphs are a way to connect, organize, and access disparate information about diseases with numerous benefits for artificial intelligence (AI). To create knowledge graphs, it is necessary to extract knowledge from multimodal datasets in the form of relationships between disease concepts and normalize both concepts and relationship types. Methods: We introduce REMAP, a multimodal approach for disease relation extraction and classification. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, followed by aligning the multimodal embeddings for optimal disease relation extraction. Results: We apply REMAP approach to a disease knowledge graph with 96,913 relations and a text dataset of 1.24 million sentences. On a dataset annotated by human experts, REMAP improves text-based disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with text information. Further, REMAP leverages text information to recommend new relationships in the knowledge graph, outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score). Conclusion: REMAP is a multimodal approach for extracting and classifying disease relationships by fusing structured knowledge and text information. REMAP provides a flexible neural architecture to easily find, access, and validate AI-driven relationships between disease concepts.
