Extracting medical information from narrative patient records: the case of medication-related information
Extracting medical information from narrative patient records: the case of medication-related information
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DOI：
10.1136/jamia.2010.003962
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发表时间：
2010-09-01
影响因子：
6.4
通讯作者：
Zweigenbaum, Pierre
中科院分区：
文献类型：
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作者：
Deleger, Louise;Grouin, Cyril;Zweigenbaum, Pierre
目的 虽然对患者护理至关重要，但与药物相关的信息通常以自由文本形式写入临床记录中，因此难以在计算机系统中使用。本文描述了一种从临床记录中自动提取药物信息的方法，该方法是为参加 i2b2 2009 挑战赛而开发的，以及改进提取的不同策略。设计我们的方法依赖于语义词典和提取规则作为两阶段策略：首先，识别药物名称，然后探索这些名称的上下文，根据捕获文档结构和每种信息的语法的规则来提取药物相关信息（模式、剂量等）。测试了不同的配置，以在多个维度上改进该基线系统，特别是药物名称识别，这一步骤是提取药物相关信息的决定因素。在词典和提取规则级别测试了更改。结果 参与 i2b2 的初始系统取得了良好的结果（全局 F 测量为 77%）。对不同配置的进一步测试大大改善了系统（全局 F 测量为 81%），对所有类型的信息都表现良好（例如，药物名称为 84%，模式为 88%），但持续时间和原因仍然存在问题。 结论 这项研究表明，一个简单的基于规则的系统可以在药物提取任务上取得良好的性能。我们还表明，受控修改（词典过滤和规则细化）是最能提高性能的改进。
Objective While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as well as different strategies to improve the extraction.Design Our approach relies on a semantic lexicon and extraction rules as a two-phase strategy: first, drug names are recognized and, then, the context of these names is explored to extract drug-related information (mode, dosage, etc) according to rules capturing the document structure and the syntax of each kind of information. Different configurations are tested to improve this baseline system along several dimensions, particularly drug name recognition this step being a determining factor to extract drug-related information. Changes were tested at the level of the lexicons and of the extraction rules.Results The initial system participating in i2b2 achieved good results (global F-measure of 77%). Further testing of different configurations substantially improved the system (global F-measure of 81%), performing well for all types of information (eg, 84% for drug names and 88% for modes), except for durations and reasons, which remain problematic.Conclusion This study demonstrates that a simple rule-based system can achieve good performance on the medication extraction task. We also showed that controlled modifications (lexicon filtering and rule refinement) were the improvements that best raised the performance.