Named Entity Recognition Over Electronic Health Records Through a Combined Dictionary-based Approach

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• 作者：Alexandra Pomares Quimbaya^a ; ^{pomares@javeriana.edu.co" class="auth_mail" title="E-mail the corresponding author} ; Alejandro Sierra Mú ; nera^b ; Rafael André ; s Gonzá ; lez Rivera^a ; Juliá ; n Camilo Daza Rodrí ; guez^b ; Oscar Mauricio Mu&ntilde ; oz Velandia^a ; ^b ; Angel Alberto Garcia Pe&ntilde ; a^a ; ^b ; Cyril Labbé ; ^c
• 关键词：Named Entity Recognition ; Electronic Health Records ; Text Mining
• 刊名：Procedia Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：100
• 期：Complete
• 页码：55-61
• 全文大小：278 K

文摘

In health care information systems, electronic health records are an important part of the knowledge concerning individual health histories. Extracting valuable knowledge from these records represents a challenging task because they are composed of data of different kind: images, test results, narrative texts that include both highly codified and a variety of notes which are diverse in language and detail, as well as ad hoc terminology, including acronyms and jargon, far from being highly codified. This paper proposes a combined approach for the recognition of named entities in such narrative texts. This approach is a composition of three different methods. The possible combinations are evaluated and the resulting composition shows an improvement of the recall and a limited impact on precision for the named entity recognition process.