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.
@article{arxiv.2310.20588,
title = {Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding},
author = {Yuqi Wang and Zeqiang Wang and Wei Wang and Qi Chen and Kaizhu Huang and Anh Nguyen and Suparna De},
journal= {arXiv preprint arXiv:2310.20588},
year = {2023}
}