EHR Text Categorization for Enhanced Patient-Based Document Navigation

Patients with multiple disorders usually have long diagnosis lists, constitute by ICD-10 codes together with individual free-text descriptions. These text snippets are produced by overwriting standardized ICD-Code topics by the physicians at the point of care. They provide highly compact expert desc...

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Bibliographic Details
Published inStudies in health technology and informatics Vol. 248; p. 100
Main Authors Kreuzthaler, Markus, Pfeifer, Bastian, Vera Ramos, José Antonio, Kramer, Diether, Grogger, Victor, Bredenfeldt, Sylvia, Pedevilla, Markus, Krisper, Peter, Schulz, Stefan
Format Journal Article
LanguageEnglish
Published Netherlands 2018
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Summary:Patients with multiple disorders usually have long diagnosis lists, constitute by ICD-10 codes together with individual free-text descriptions. These text snippets are produced by overwriting standardized ICD-Code topics by the physicians at the point of care. They provide highly compact expert descriptions within a 50-character long text field frequently not assigned to a specific ICD-10 code. The high redundancy of these lists would benefit from content-based categorization within different hospital-based application scenarios. This work demonstrates how to accurately group diagnosis lists via a combination of natural language processing and hierarchical clustering with an overall F-measure value of 0.87. In addition, it compresses the initial diagnosis list up to 89%. The manuscript discusses pitfall and challenges as well as the potential of a large-scale approach for tackling this problem.
ISSN:0926-9630
DOI:10.3233/978-1-61499-858-7-100