基于本体的医生自由文本记录中临床信息抽取。

Ontology-based clinical information extraction from physician's free-text notes.

机构信息

Information Systems Department, Faculty of Computers and Information, Helwan University, Helwan, Cairo, Egypt; Business Information Systems Department, Faculty of Commerce and Business Administration, Helwan University, Helwan, Cairo, Egypt.

General Surgery Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt.

出版信息

J Biomed Inform. 2019 Oct;98:103276. doi: 10.1016/j.jbi.2019.103276. Epub 2019 Aug 29.

DOI:10.1016/j.jbi.2019.103276

PMID:31473365

Abstract

Documenting clinical notes in electronic health records might affect physician's workflow. In this paper, an Ontology-based clinical information extraction system, OB-CIE, has been developed. OB-CIE system provides a method for extracting clinical concepts from physician's free-text notes and converts the unstructured clinical notes to structured information to be accessed in electronic health records. OB-CIE system can help physicians to document visit notes without changing their workflow. For recognizing named entities of clinical concepts, ontology concepts have been used to construct a dictionary of semantic categories, then, exact dictionary matching method has been used to match noun phrases to their semantic categories. A rule-based approach has been used to classify clinical sentences to their predefined categories. The system evaluation results have achieved an F-measure of 94.90% and 97.80% for concepts classification and sentences classification, respectively. The results have showed that OB-CIE system performed well on extracting clinical concepts compared with data mining techniques. The system can be used in another field by adapting its ontology and extraction rule set.

摘要

在电子健康记录中记录临床笔记可能会影响医生的工作流程。在本文中，开发了一种基于本体的临床信息提取系统 OB-CIE。OB-CIE 系统提供了一种从医生的自由文本记录中提取临床概念的方法，并将非结构化的临床记录转换为可在电子健康记录中访问的结构化信息。OB-CIE 系统可以帮助医生在不改变工作流程的情况下记录就诊记录。为了识别临床概念的命名实体，本体概念被用于构建语义类别词典，然后使用精确词典匹配方法将名词短语与其语义类别进行匹配。基于规则的方法用于将临床句子分类到预定义的类别中。系统评估结果表明，概念分类和句子分类的 F 度量分别达到了 94.90%和 97.80%。结果表明，与数据挖掘技术相比，OB-CIE 系统在提取临床概念方面表现良好。通过调整其本体和提取规则集，该系统可以在另一个领域中使用。