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)![](/opac-tmpl/bootstrap/images/filefind.png)
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Item type | Current library | Call number | Copy number | Status | Date due | Barcode | Item holds |
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ATU Sligo Yeats Library Main Lending Collection | 616.994075 TAK (Browse shelf(Opens below)) | 1 | Available | 0068914 |
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616.994 SHI Racial and geographical factors in tumour incidence / | 616.99406 MOU Cancer : the healthy option / | 616.994071 CLA Toxicological carcinogenesis / | 616.994075 TAK Outcome prediction in cancer / | 616.99449 PLA Your life in your hands : understanding, preventing and overcoming breast cancer / | 617.03 GAL Psychoprosthetics / | 617.1027 Sports injuries : their prevention and treatment / |
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.