Effective Free-Text Medical Record Processing and Information Retrieval

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00193461" target="_blank" >RIV/68407700:21230/12:00193461 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/12:00064004

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-29305-4_342" target="_blank" >http://dx.doi.org/10.1007/978-3-642-29305-4_342</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-29305-4_342" target="_blank" >10.1007/978-3-642-29305-4_342</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Effective Free-Text Medical Record Processing and Information Retrieval

Popis výsledku v původním jazyce

Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules. The task becomes even more difficult when natural language is used and no apriori knowledge is available. The medical environment itself is also very specific: the natural language used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e. medici terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations. This paper describes the process of mining informatik from loosely structured medicaltextual records with no apriori knowledge. In the paper we depict the process of mining a large dataset of ~50,000-120,000 records x 20 attributes in database tables, originating from the hospital information systém (thanks go to the Univ

Název v anglickém jazyce

Effective Free-Text Medical Record Processing and Information Retrieval

Popis výsledku anglicky

Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules. The task becomes even more difficult when natural language is used and no apriori knowledge is available. The medical environment itself is also very specific: the natural language used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e. medici terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations. This paper describes the process of mining informatik from loosely structured medicaltextual records with no apriori knowledge. In the paper we depict the process of mining a large dataset of ~50,000-120,000 records x 20 attributes in database tables, originating from the hospital information systém (thanks go to the Univ

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/NT11124" target="_blank" >NT11124: Vliv hodnocení kardiotokografie pomocí metod umělé inteligence na kvalitu perinatální péče</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IFMBE Proceedings: World Congress on Medical Physics and Biomedical Engineering

ISBN

978-3-642-29304-7

ISSN

1680-0737

e-ISSN

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Počet stran výsledku

4

Strana od-do

1305-1308

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Beijing ( Peking)

Datum konání akce

26. 5. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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