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Outcome prediction in cancer /

Contributor(s): Taktak, Azzam F. G | Fisher, Anthony C., Dr.
Publisher: Amsterdam ; Boston : Elsevier, c2007Description: xx, 461 p. : ill. ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780444528551; 9780444528551 :; 0444528555.Subject(s): Cancer -- Diagnosis | Neural networks (Computer science) | Cancer -- Prognosis | Survival analysis (Biometry)DDC classification: 616.994075
Contents:
The predictive value of detailed histological staging of surgical resection specimens in oral cancer -- Survival after treatment of intraocular melanoma -- Recent developments in relative survival analysis -- Environmental and genetic risk factors of lung cancer -- Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer -- Flexible hazard modelling for outcome prediction in cancer: perspectives for the use of bioinformatics knowledge -- Information geometry for survival analysis and feature selection by neural networks -- Artificial neural networks used in the survival analysis of breast cancer patients: a node-negative study -- The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients -- Machine learning contribution to solve prognostic medical problems -- Classification of brain tumors by pattern recognition of magnetic resonance imaging and spectroscopic data -- Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data -- The impact of microarray technology in brain cancer -- The web and the new generation of medical information systems -- Geoconda: a web environment for multi-centre research -- The development and execution of medical prediction models.
Summary: Organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. This work describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. It discusses a number of machine learning methods which have been applied to decision support in cancer.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Standard Loan Standard Loan ATU Sligo Yeats Library Main Lending Collection 616.994075 TAK (Browse shelf(Opens below)) 1 Available 0068914
Total holds: 0

Includes bibliographical references and index.

The predictive value of detailed histological staging of surgical resection specimens in oral cancer -- Survival after treatment of intraocular melanoma -- Recent developments in relative survival analysis -- Environmental and genetic risk factors of lung cancer -- Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer -- Flexible hazard modelling for outcome prediction in cancer: perspectives for the use of bioinformatics knowledge -- Information geometry for survival analysis and feature selection by neural networks -- Artificial neural networks used in the survival analysis of breast cancer patients: a node-negative study -- The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients -- Machine learning contribution to solve prognostic medical problems -- Classification of brain tumors by pattern recognition of magnetic resonance imaging and spectroscopic data -- Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data -- The impact of microarray technology in brain cancer -- The web and the new generation of medical information systems -- Geoconda: a web environment for multi-centre research -- The development and execution of medical prediction models.

Organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. This work describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. It discusses a number of machine learning methods which have been applied to decision support in cancer.

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