Big Data, medizinische Sprache und biomedizinische Ordnungssysteme

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• 作者：Stefan Schulz ; Pablo López-García
• 关键词：Biomedizinische Terminologie ; Computergestützte Verarbeitung menschlicher Sprache ; Ontologie ; Big Data ; Elektronische Krankenakten ; Biomedical terminology ; Natural language processing ; Ontology ; Big data ; Electronic health records
• 刊名：Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：58
• 期：8
• 页码：844-852
• 全文大小：364 KB
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• 作者单位：Stefan Schulz (1)
  Pablo López-García (1)
  
  1. Institut für Medizinische Informatik, Statistik und Dokumentation, Medizinische Universit?t Graz, Auenbruggerplatz 2/V, 8036, Graz, ?sterreich
  
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  General Practice and Family Medicine
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1437-1588

文摘

A variety of rich terminology systems, such as thesauri, classifications, nomenclatures and ontologies support information and knowledge processing in health care and biomedical research. Nevertheless, human language, manifested as individually written texts, persists as the primary carrier of information, in the description of disease courses or treatment episodes in electronic medical records, and in the description of biomedical research in scientific publications. In the context of the discussion about big data in biomedicine, we hypothesize that the abstraction of the individuality of natural language utterances into structured and semantically normalized information facilitates the use of statistical data analytics to distil new knowledge out of textual data from biomedical research and clinical routine. Computerized human language technologies are constantly evolving and are increasingly ready to annotate narratives with codes from biomedical terminology. However, this depends heavily on linguistic and terminological resources. The creation and maintenance of such resources is labor-intensive. Nevertheless, it is sensible to assume that big data methods can be used to support this process. Examples include the learning of hierarchical relationships, the grouping of synonymous terms into concepts and the disambiguation of homonyms. Although clear evidence is still lacking, the combination of natural language technologies, semantic resources, and big data analytics is promising.