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Description
  • V minulosti byl problém extrakce informací o pacientovi ze zdravotního záznamu řešen ručním vytvářením extrakčních pravidel pro každou položku, která měla být extrahována. Tento přístup však při změně množiny extrahovaných údajů nebo textového korpusu vyžaduje mnoho času a dozor zkušeného programátora. V tomto článku zkoumáme možnosti, jak tento proces mechanizovat za pomocí automatického generování extrakčních pravidel na základě předem anotovaného korpusu medicínských textů. (cs)
  • In the past, the problem of extracting patient data from medical record was solved by writing extraction rules for every element of information that is to be extracted. However, in general such approach is very time consuming and requires supervision of a skilled programmer whenever the target area of medicine or the text corpus are changed. In this article we explore the possibility to mechanize this process by automatically generating the extraction rules from a pre-annotated corpus of medical texts.
  • In the past, the problem of extracting patient data from medical record was solved by writing extraction rules for every element of information that is to be extracted. However, in general such approach is very time consuming and requires supervision of a skilled programmer whenever the target area of medicine or the text corpus are changed. In this article we explore the possibility to mechanize this process by automatically generating the extraction rules from a pre-annotated corpus of medical texts. (en)
Title
  • How to Retrieve Knowledge from Medical Texts Effectively
  • How to Retrieve Knowledge from Medical Texts Effectively (en)
  • Jak efektivně získávat znalosti z medicínských textů (cs)
skos:prefLabel
  • How to Retrieve Knowledge from Medical Texts Effectively
  • How to Retrieve Knowledge from Medical Texts Effectively (en)
  • Jak efektivně získávat znalosti z medicínských textů (cs)
skos:notation
  • RIV/67985807:_____/05:00405656!RIV06-AV0-67985807
http://linked.open.../vavai/riv/strany
  • 1;6
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET200300413), Z(AV0Z10300504)
http://linked.open...iv/cisloPeriodika
  • -
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
  • 523804
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/05:00405656
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • information extraction; natural language processing; machine learning (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • SE - Švédské království
http://linked.open...ontrolniKodProRIV
  • [C81790820871]
http://linked.open...i/riv/nazevZdroje
  • IFMBE Proceedings
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 11
http://linked.open...iv/tvurceVysledku
  • Kolesa, Petr
  • Ďurovec, Ján
  • Antolík, Ján
http://linked.open...n/vavai/riv/zamer
issn
  • 1727-1983
number of pages
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