Multimodal Learning on Graphs for Disease Relation Extraction

• Main Authors: Lin, Yucong, Lu, Keming, Yu, Sheng, Cai, Tianxi, Zitnik, Marinka
• Format: Journal Article
• Language: English
• Published: 16-03-2022
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computation and Language; Computer Science - Learning; Computer Science - Social and Information Networks
• DOI: 10.48550/arxiv.2203.08893

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.