Clinical text mining for efficient extraction of drug-allergy reactions

Arantza Casillas, Koldo Gojenola, Alicia Pérez, M. Oronoz
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引用次数: 6

Abstract

This work focuses on the extraction of allergic drug reactions in electronic health records. The goal is to annotate a sub-class of cause-effect events, those in which drugs are causing allergies. Little work has carried out in this field, seldom for Spanish clinical text mining, which is, indeed, the aim of this work. We present two approaches: a rule-based method and another one based on machine learning. Both approaches incorporate semantic knowledge derived from FreeLing-Med, a software explicitly developed to parse texts in the medical domain. Having recognised the medical entities for a given record, the challenge stands on triggering the underlying allergies. To this end, the knowledge is expressed as a set of semantic, syntactic and structural features. Our best system, based on machine learning, obtained a precision of 0.90 with a recall of 0.87, outperforming a rule-based approach.
有效提取药物过敏反应的临床文本挖掘
本研究的重点是电子病历中药物过敏反应的提取。目标是注释因果事件的一个子类,即那些药物引起过敏的事件。在这一领域开展的工作很少,很少用于西班牙临床文本挖掘,这确实是本工作的目的。我们提出了两种方法:基于规则的方法和另一种基于机器学习的方法。这两种方法都结合了源自FreeLing-Med的语义知识,FreeLing-Med是一种明确开发用于解析医学领域文本的软件。在确认了给定记录的医疗实体之后,挑战在于触发潜在的过敏。为此,知识被表达为一组语义、句法和结构特征。我们最好的系统,基于机器学习,获得了0.90的精度和0.87的召回率,优于基于规则的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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