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
Subjects
Electronic Health Records
Humans
International Classification of Diseases
Natural Language Processing
Semantics
Electronic Health Records
Natural Language Processing
Cluster Analysis
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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