  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.
