Machine Learning?Based Knowledge Extraction from Complex Clinical Oncological Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F06%3A00018920" target="_blank" >RIV/00216224:14310/06:00018920 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Machine Learning?Based Knowledge Extraction from Complex Clinical Oncological Data

Original language description

The article describes some bio?informatics problems and results achieved by application of selected machine?learning tools to extracting knowledge from relatively difficult clinical oncological data. The structure of the clinical data allows detailed analyses of epidemiological and clinical aspects. Performed analyses can provide significant predictions not only for diagnostic risk factors but also for the applied therapeutic strategy. Despite the complexity and issues of the particular acute?leukemia data, experimental results demonstrated good applicability of the tools (as the automatically generated decision trees and rules) to certain difficult problems with predictions, or looking for relevant attributes. Naturally, many problems are still waiting for solutions.

Czech name

Extrakce znalostí z komplexních klinických onkologických dat strojovým učením

Czech description

The article describes some bio?informatics problems and results achieved by application of selected machine?learning tools to extracting knowledge from relatively difficult clinical oncological data. The structure of the clinical data allows detailed analyses of epidemiological and clinical aspects. Performed analyses can provide significant predictions not only for diagnostic risk factors but also for the applied therapeutic strategy. Despite the complexity and issues of the particular acute?leukemia data, experimental results demonstrated good applicability of the tools (as the automatically generated decision trees and rules) to certain difficult problems with predictions, or looking for relevant attributes. Naturally, many problems are still waiting for solutions.

Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/NR8080" target="_blank" >NR8080: Predictive evaluation of prognostic factors focussing on genetics and treatment stratification,transplantation of haematopoetic stem cells included,in standardized national register of acute leukemias. Keeping on data reporting and management continuity.</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2006

Confidentiality

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

Article name in the collection

Knowledge Extraction and Modeling KNEMO-2006

ISBN

88-89744-01-4

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

341-352

Publisher name

Tilapia Edizioni

Place of publication

Italia

Event location

Villa Orlandi, Anacapri, Capri, Italia

Event date

Sep 4, 2006

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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