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Description
  • Machine learning and data mining (DM) offer numerous semi-automated methods how to find characteristic patterns and gain better insight into data records stored in computer databases. DM cannot be applied directly to the medical records, since their basic format is a text file written in a natural language. Therefore it is necessary to transform the data in patients' text files into a structured form by means of semantic annotation. We propose a method for semi-automated semantic annotation consisting of named entity recognition using conditional random fields and manual annotation of relations.
  • Machine learning and data mining (DM) offer numerous semi-automated methods how to find characteristic patterns and gain better insight into data records stored in computer databases. DM cannot be applied directly to the medical records, since their basic format is a text file written in a natural language. Therefore it is necessary to transform the data in patients' text files into a structured form by means of semantic annotation. We propose a method for semi-automated semantic annotation consisting of named entity recognition using conditional random fields and manual annotation of relations. (en)
  • Metody strojového učení nemohou být aplikovány přímo na lékařské zprávy, které jsou většinou ve formě textových souborů. Proto je nutné převést informace obsažené v lékařských zprávách do strukturované podoby. Přestavujeme systém pro semi-automatickou sémantickou anotaci lékařských záznamů, která spočívá v automatické extrakci pojmů s využitím metody podmíněných náhodných polí a manuální extrakci vztahů mezi nimi. (cs)
Title
  • Semantic Annotation of Medical Records
  • Semantic Annotation of Medical Records (en)
  • Sémantická anotace lékařských záznamů (cs)
skos:prefLabel
  • Semantic Annotation of Medical Records
  • Semantic Annotation of Medical Records (en)
  • Sémantická anotace lékařských záznamů (cs)
skos:notation
  • RIV/68407700:21230/07:03129168!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 449347
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/07:03129168
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • information extraction; named entity recognition; semantic annotation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [963853865638]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Praha
http://linked.open...i/riv/nazevZdroje
  • Proceedings of Workshop 2007
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Žáková, Monika
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • České vysoké učení technické v Praze
https://schema.org/isbn
  • 978-80-01-03667-9
http://localhost/t...ganizacniJednotka
  • 21230
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