Selecting relevant information for medical decision support with application in cardiology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10194313" target="_blank" >RIV/00216208:11320/13:10194313 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/13:00395155 RIV/00216208:11110/13:10194313

Výsledek na webu

<a href="http://www.ejbi.org/img/ejbi/2013/1/Kalina_en.pdf" target="_blank" >http://www.ejbi.org/img/ejbi/2013/1/Kalina_en.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Selecting relevant information for medical decision support with application in cardiology

Popis výsledku v původním jazyce

Objectives: The aim of our work was to implement a prototype of a decision support system which has the form of a web-based classification service. Because the data analysis component of decision support systems often happens to be unsuitable for high-dimensional data, special attention must be paid to the sophisticated selection of the most relevant variables before learning the classification rule. Methods: We implemented a prototype of a diagnostic decision support system called SIR. The system has the ability to select the most relevant variables based on a set of high-dimensional measurements by means of a forward procedure optimizing a decision-making criterion. This allows to learn a reliable classification rule. Results: The implemented prototype was tested on a sample of patients involved in a cardiology study. We used SIR to perform an information extraction from a cardiological clinical study containing both clinical and gene expression data. The classification performance w

Název v anglickém jazyce

Selecting relevant information for medical decision support with application in cardiology

Popis výsledku anglicky

Objectives: The aim of our work was to implement a prototype of a decision support system which has the form of a web-based classification service. Because the data analysis component of decision support systems often happens to be unsuitable for high-dimensional data, special attention must be paid to the sophisticated selection of the most relevant variables before learning the classification rule. Methods: We implemented a prototype of a diagnostic decision support system called SIR. The system has the ability to select the most relevant variables based on a set of high-dimensional measurements by means of a forward procedure optimizing a decision-making criterion. This allows to learn a reliable classification rule. Results: The implemented prototype was tested on a sample of patients involved in a cardiology study. We used SIR to perform an information extraction from a cardiological clinical study containing both clinical and gene expression data. The classification performance w

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/1M06014" target="_blank" >1M06014: Centrum biomedicínské informatiky (CBI)</a><br>

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Europaen Journal of Biomedical Informatics

ISSN

1801-5603

e-ISSN

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Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

5

Strana od-do

2-6

Kód UT WoS článku

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EID výsledku v databázi Scopus

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