Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding
Yuqi Wang Zeqiang Wang Wei Wang Qi Chen Kaizhu Huang Anh Nguyen Suparna De
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.