Evaluating Entity Retrieval in Electronic Health Records: a Semantic Gap Perspective

• URL: http://arxiv.org/abs/2502.06252v1
• Date: Mon, 10 Feb 2025 08:33:47 GMT
• Title: Evaluating Entity Retrieval in Electronic Health Records: a Semantic Gap Perspective
• Authors: Zhengyun Zhao, Hongyi Yuan, Jingjing Liu, Haichao Chen, Huaiyuan Ying, Songchi Zhou, Sheng Yu,
• Abstract summary: We propose the development and release of a novel benchmark for evaluating entity retrieval in EHRs.<n>We use discharge summaries from the MIMIC-III dataset, generate 1,246 queries, and provide over 77,000 relevance annotations.<n>To offer the first assessment of the semantic gap, we introduce a novel classification system for relevance matches.
• Score: 11.786980537459405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Entity retrieval plays a crucial role in the utilization of Electronic Health Records (EHRs) and is applied across a wide range of clinical practices. However, a comprehensive evaluation of this task is lacking due to the absence of a public benchmark. In this paper, we propose the development and release of a novel benchmark for evaluating entity retrieval in EHRs, with a particular focus on the semantic gap issue. Using discharge summaries from the MIMIC-III dataset, we incorporate ICD codes and prescription labels associated with the notes as queries, and annotate relevance judgments using GPT-4. In total, we use 1,000 patient notes, generate 1,246 queries, and provide over 77,000 relevance annotations. To offer the first assessment of the semantic gap, we introduce a novel classification system for relevance matches. Leveraging GPT-4, we categorize each relevant pair into one of five categories: string, synonym, abbreviation, hyponym, and implication. Using the proposed benchmark, we evaluate several retrieval methods, including BM25, query expansion, and state-of-the-art dense retrievers. Our findings show that BM25 provides a strong baseline but struggles with semantic matches. Query expansion significantly improves performance, though it slightly reduces string match capabilities. Dense retrievers outperform traditional methods, particularly for semantic matches, and general-domain dense retrievers often surpass those trained specifically in the biomedical domain.

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