Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding

• URL: http://arxiv.org/abs/2310.20588v1
• Date: Tue, 31 Oct 2023 16:26:33 GMT
• Title: Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding
• Authors: Yuqi Wang, Zeqiang Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, and Suparna De
• Abstract summary: This paper introduces MedFusionRank, a novel approach to zero-shot medical information retrieval (MIR) The proposed approach leverages a pre-trained BERT-style model to extract compact yet informative keywords. These keywords are then enriched with domain knowledge by linking them to conceptual entities within a medical knowledge graph.
• Score: 27.14794371879541
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making. This paper introduces MedFusionRank, a novel approach to zero-shot medical information retrieval (MIR) that combines the strengths of pre-trained language models and statistical methods while addressing their limitations. The proposed approach leverages a pre-trained BERT-style model to extract compact yet informative keywords. These keywords are then enriched with domain knowledge by linking them to conceptual entities within a medical knowledge graph. Experimental evaluations on medical datasets demonstrate MedFusion Rank's superior performance over existing methods, with promising results with a variety of evaluation metrics. MedFusionRank demonstrates efficacy in retrieving relevant information, even from short or single-term queries.

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