Practical Problems and Solutions in Hospital Information System Data Mining

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00194641" target="_blank" >RIV/68407700:21230/12:00194641 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/12:00064000

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-642-32395-9_3?null" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-642-32395-9_3?null</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Practical Problems and Solutions in Hospital Information System Data Mining

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.Doctors often use natural language in medical records.Therefore it contains many ambiguities due to non-standard abbreviations and synonyms. Themedical environment itself is also very specific: the natural langure used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e.medical 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 information from loosely structured medical textual records with no apriori knowledge. The paper concerns mining a large dataset of ~50,000-140,000 records x 20 attributes in relational database tables, originating from the hospitál information system

Název v anglickém jazyce

Practical Problems and Solutions in Hospital Information System Data Mining

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.Doctors often use natural language in medical records.Therefore it contains many ambiguities due to non-standard abbreviations and synonyms. Themedical environment itself is also very specific: the natural langure used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e.medical 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 information from loosely structured medical textual records with no apriori knowledge. The paper concerns mining a large dataset of ~50,000-140,000 records x 20 attributes in relational database tables, originating from the hospitál information system

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

Information Technology in Bio- and Medical Informatics

ISBN

978-3-642-32394-2

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

9

Strana od-do

31-39

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Vienna

Datum konání akce

4. 9. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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