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Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F05%3A03113280" target="_blank" >RIV/68407700:21230/05:03113280 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Mining the Strongest Patterns in Medical Sequential Data

Popis výsledku v původním jazyce

Sequential data represent an important source of automatically mined and potentially new medical knowledge. They can originate in various ways. Within the presented domain they come from a longitudinal preventive study of atherosclerosis - the data consist of series of long-term observations recording the development of risk factors and associated conditions. The intention is to identify frequent sequential patterns having any relation to an onset of any of the observed cardiovascular diseases. This paper focuses on application of inductive logic programming. The prospective patterns are based on first-order features automatically extracted from the sequential data. The features are further grouped in order to reach final complex patterns expressed asrules. The presented approach is also compared with the approaches published earlier (windowing, episode rules).

Název v anglickém jazyce

Mining the Strongest Patterns in Medical Sequential Data

Popis výsledku anglicky

Sequential data represent an important source of automatically mined and potentially new medical knowledge. They can originate in various ways. Within the presented domain they come from a longitudinal preventive study of atherosclerosis - the data consist of series of long-term observations recording the development of risk factors and associated conditions. The intention is to identify frequent sequential patterns having any relation to an onset of any of the observed cardiovascular diseases. This paper focuses on application of inductive logic programming. The prospective patterns are based on first-order features automatically extracted from the sequential data. The features are further grouped in order to reach final complex patterns expressed asrules. The presented approach is also compared with the approaches published earlier (windowing, episode rules).

Druh

A - Audiovizuální tvorba

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/KJB201210501" target="_blank" >KJB201210501: Logické strojové učení pro analýzu genomických dat</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2005

Kód důvěrnosti údajů

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

ISBN

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Místo vydání

Praha

Název nakladatele resp. objednatele

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Verze

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Identifikační číslo nosiče

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