Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding
This addresses efficient clinical decision-making in IoT contexts by improving retrieval from short queries, though it appears incremental as it builds on existing techniques.
The paper tackles medical information retrieval by proposing MedFusionRank, a zero-shot approach that combines pre-trained language models with knowledge graphs to extract and enrich keywords. It shows superior performance over existing methods on medical datasets with various evaluation metrics.
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.