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将本地放射学术语自动映射到LOINC。

Automated mapping of local radiology terms to LOINC.

作者信息

Vreeman Daniel J, McDonald Clement J

机构信息

Regenstrief Institute, Inc. and Indiana University School of Medicine, Indianapolis, IN, USA.

出版信息

AMIA Annu Symp Proc. 2005;2005:769-73.

Abstract

We developed an automated tool, called the Intelligent Mapper (IM), to improve the efficiency and consistency of mapping local terms to LOINC. We evaluated IM's performance in mapping diagnostic radiology report terms from two hospitals to LOINC by comparing IM's term rankings to a manually established gold standard. Using a CPT-based restriction, for terms with a LOINC code match, IM ranked the correct LOINC code first in 90% of our development set terms, and in 87% of our test set terms. The CPT-based restriction significantly improved IM's ability to identify correct LOINC codes. We have made IM freely available, with the aim of reducing the effort required to integrate disparate systems and helping move us towards the goal of interoperable health information exchange.

摘要

我们开发了一种名为智能映射器(IM)的自动化工具,以提高将本地术语映射到LOINC的效率和一致性。我们通过将IM的术语排名与手动建立的黄金标准进行比较,评估了IM在将两家医院的诊断放射学报告术语映射到LOINC方面的性能。使用基于CPT的限制,对于与LOINC代码匹配的术语,IM在我们开发集术语的90%以及测试集术语的87%中,将正确的LOINC代码排在首位。基于CPT的限制显著提高了IM识别正确LOINC代码的能力。我们已免费提供IM,旨在减少整合不同系统所需的工作量,并帮助我们朝着可互操作的健康信息交换目标迈进。

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