Big Data, medizinische Sprache und biomedizinische Ordnungssysteme

Abstract

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.

Digitale Gesundheitsanwendungen – Rahmenbedingungen zur Nutzung in Versorgung, Strukturentwicklung und Wissenschaft – Positionspapier der AG Digital Health des DNVF

“…Eine der wichtigsten wissenschaftlichen Leistungen ist die Reduktion auf wesentliche Aspekte, die nie voraussetzungslos, sondern immer auf der Basis von Vorüberlegungen stattfindet. Allein schon die sprachliche Fassung eines Ergebnisses erfordert eine Reduktion auf Begriffe, auch wenn insinuiert wird, dass auch diese Schwierigkeit bei genügendem Einsatz semantischer Instrumente durch Big Data Analysen ersetzt werden könne [50].…”