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.

翻译：在物联网时代，相关医学信息的检索对于高效的临床决策至关重要。本文提出MedFusionRank，一种新颖的零样本医学信息检索方法，该方法融合了预训练语言模型与统计方法的优势，同时克服了各自的局限性。所提出的方法利用预训练的BERT模型提取紧凑且信息丰富的关键词，随后通过将这些关键词与医学知识图谱中的概念实体关联，丰富其领域知识。在医学数据集上的实验评估表明，MedFusionRank在多种评估指标下均优于现有方法，展现出显著性能优势。即使面对简短或单词查询，该方法仍能有效检索相关信息。