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Shallow Medication Extraction from Hospital Patient Records

机译:从医院患者记录中浅层提取药物

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摘要

This paper presents methods for shallow Information Extraction (IE) from the free text zones of hospital Patient Records (PRs) in Bulgarian language in the Patient Safety through Intelligent Procedures in medication (PSIP) project. We extract automatically information about drug names, dosage, modes and frequency and assign the corresponding ATC code to each medication event. Using various modules for rule-based text analysis, our IE components in PSIP perform a significant amount of symbolic computations. We try to address negative statements, elliptical constructions, typical conjunctive phrases, and simple inferences concerning temporal constraints and finally aim at the assignment of the drug ACT code to the extracted medication events, which additionally complicates the extraction algorithm. The prototype of the system was used for experiments with a training corpus containing 1,300 PRs and the evaluation results are obtained using a test corpus containing 6,200 PRs. The extraction accuracy (f-score) for drug names is 98.42% and for dose 93.85%.
机译:本文介绍了通过药物智能程序(PSIP)从患者安全中以保加利亚语从医院患者记录(PRs)的自由文本区域中进行浅层信息提取(IE)的方法。我们会自动提取有关药物名称,剂量,方式和频率的信息,并为每个用药事件分配相应的ATC代码。 PSIP中的IE组件使用各种模块进行基于规则的文本分析,从而执行了大量的符号计算。我们试图解决否定性陈述,椭圆结构,典型的连词短语以及有关时间限制的简单推论,最后针对将药物ACT代码分配给提取的用药事件,这使提取算法更加复杂。该系统的原型用于包含1300个PR的训练语料库的实验,并使用包含6200个PR的测试语料获得评估结果。药物名称的提取准确度(f评分)为98.42%,剂量为93.85%。

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