Obstetric 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%3A00201789" target="_blank" >RIV/68407700:21230/12:00201789 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-29262-0_4#" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-29262-0_4#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-29262-0_4" target="_blank" >10.1007/978-3-642-29262-0_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Obstetric Medical Record Processing and Information Retrieval

Popis výsledku v původním jazyce

This paper describes the process of mining information from loosely structured medical textual 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 system (thanks go to the University Hospital in Brno, Czech Republic) recording over 10 years. This paper concerns only textual attributes with free text input, that means 613,000 text fields in 16 attributes. Each attribute item contains ~800-1,500 characters (diagnoses, medications, etc.). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining and automated processing. 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 ve

Název v anglickém jazyce

Obstetric Medical Record Processing and Information Retrieval

Popis výsledku anglicky

This paper describes the process of mining information from loosely structured medical textual 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 system (thanks go to the University Hospital in Brno, Czech Republic) recording over 10 years. This paper concerns only textual attributes with free text input, that means 613,000 text fields in 16 attributes. Each attribute item contains ~800-1,500 characters (diagnoses, medications, etc.). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining and automated processing. 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 ve

Druh

C - Kapitola v odborné knize

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 knihy nebo sborníku

Electronic Healthcare

ISBN

978-3-642-29261-3

Počet stran výsledku

8

Strana od-do

26-33

Počet stran knihy

205

Název nakladatele

Springer

Místo vydání

Dordrecht

Kód UT WoS kapitoly

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