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Improving patient record search: A meta-data based approach

机译:改善患者记录搜索:基于元数据的方法

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摘要

The International Classification of Diseases (ICD) is a type of meta-data found in many Electronic Patient Records. Research to explore the utility of these codes in medical Information Retrieval (IR) applications is new, and many areas of investigation remain, including the question of how reliable the assignment of the codes has been. This paper proposes two uses of the ICD codes in two different contexts of search: Pseudo-Relevance Judgments (PRJ) and Pseudo-Relevance Feedback (PRF). We find that our approach to evaluate the TREC challenge runs using simulated relevance judgments has a positive correlation with the TREC official results, and our proposed technique for performing PRF based on the ICD codes significantly outperforms a traditional PRF approach. The results are found to be consistent over the two years of queries from the TREC medical test collection.
机译:国际疾病分类(ICD)是许多电子病历中发现的一种元数据。探索这些代码在医学信息检索(IR)应用中的效用的研究是新的,并且仍然有许多研究领域,包括代码分配的可靠性如何。本文提出了在两种不同的搜索上下文中使用ICD代码的两种方法:伪相关判断(PRJ)和伪相关反馈(PRF)。我们发现,使用模拟的相关性判断来评估TREC挑战运行的方法与TREC官方结果有正相关,并且我们提出的基于ICD代码执行PRF的技术明显优于传统PRF方法。在两年的TREC医学测试收集查询中,发现结果是一致的。

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