. "Jak efektivn\u011B z\u00EDsk\u00E1vat znalosti z medic\u00EDnsk\u00FDch text\u016F"@cs . . . "IFMBE Proceedings" . . . . "RIV/67985807:_____/05:00405656!RIV06-AV0-67985807" . . "Kolesa, Petr" . . "V minulosti byl probl\u00E9m extrakce informac\u00ED o pacientovi ze zdravotn\u00EDho z\u00E1znamu \u0159e\u0161en ru\u010Dn\u00EDm vytv\u00E1\u0159en\u00EDm extrak\u010Dn\u00EDch pravidel pro ka\u017Edou polo\u017Eku, kter\u00E1 m\u011Bla b\u00FDt extrahov\u00E1na. Tento p\u0159\u00EDstup v\u0161ak p\u0159i zm\u011Bn\u011B mno\u017Einy extrahovan\u00FDch \u00FAdaj\u016F nebo textov\u00E9ho korpusu vy\u017Eaduje mnoho \u010Dasu a dozor zku\u0161en\u00E9ho program\u00E1tora. V tomto \u010Dl\u00E1nku zkoum\u00E1me mo\u017Enosti, jak tento proces mechanizovat za pomoc\u00ED automatick\u00E9ho generov\u00E1n\u00ED extrak\u010Dn\u00EDch pravidel na z\u00E1klad\u011B p\u0159edem anotovan\u00E9ho korpusu medic\u00EDnsk\u00FDch text\u016F."@cs . "3"^^ . "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 . "\u010Eurovec, J\u00E1n" . . "Antol\u00EDk, J\u00E1n" . "Antol\u00EDk, J\u00E1n" . "How to Retrieve Knowledge from Medical Texts Effectively" . . "6"^^ . "How to Retrieve Knowledge from Medical Texts Effectively" . "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." . . "3"^^ . . "523804" . . . "1727-1983" . "Jak efektivn\u011B z\u00EDsk\u00E1vat znalosti z medic\u00EDnsk\u00FDch text\u016F"@cs . . "11" . "-" . "information extraction; natural language processing; machine learning"@en . "How to Retrieve Knowledge from Medical Texts Effectively"@en . "SE - \u0160v\u00E9dsk\u00E9 kr\u00E1lovstv\u00ED" . . "[C81790820871]" . "RIV/67985807:_____/05:00405656" . . "1;6" . . "How to Retrieve Knowledge from Medical Texts Effectively"@en . "P(1ET200300413), Z(AV0Z10300504)" .