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The Resource Outcome prediction in cancer, editors, Azzam F.G. Taktak and Anthony C. Fisher

Outcome prediction in cancer, editors, Azzam F.G. Taktak and Anthony C. Fisher

Label
Outcome prediction in cancer
Title
Outcome prediction in cancer
Statement of responsibility
editors, Azzam F.G. Taktak and Anthony C. Fisher
Contributor
Subject
Language
eng
Summary
"This book is organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. The first section describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. Amongst issues discussed in this section are the TNM staging, accepted methods for survival analysis and competing risks. The second section describes the biological and genetic markers and the role of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effective prevention and early detection strategies. The third section provides technical details of mathematical analysis behind survival prediction backed up by examples from various types of cancers. The fourth section describes a number of machine learning methods which have been applied to decision support in cancer. The final section describes how information is shared within the scientific and medical communities and with the general population using information technology and the World Wide Web."--Publisher description (LoC)
Cataloging source
NLM
Illustrations
illustrations
Index
index present
LC call number
RC270
LC item number
.O87 2007
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Taktak, Azzam F. G
  • Fisher, Anthony C.
http://library.link/vocab/subjectName
  • Cancer
  • Cancer
  • Neural networks (Computer science)
  • Survival analysis (Biometry)
  • Neoplasms
  • Prognosis
  • Decision Support Systems, Clinical
  • Neural Networks (Computer)
  • Survival Analysis
Label
Outcome prediction in cancer, editors, Azzam F.G. Taktak and Anthony C. Fisher
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
  • The
  • Geoconda: a web environment for multi-centre research
  • The
  • development and execution of medical prediction models
  • 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
Control code
ocm77482420
Dimensions
25 cm.
Extent
xx, 461 p.
Isbn
9780444528551
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
ill.
Label
Outcome prediction in cancer, editors, Azzam F.G. Taktak and Anthony C. Fisher
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
  • The
  • Geoconda: a web environment for multi-centre research
  • The
  • development and execution of medical prediction models
  • 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
Control code
ocm77482420
Dimensions
25 cm.
Extent
xx, 461 p.
Isbn
9780444528551
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
ill.

Library Locations

    • Harold Cohen LibraryBorrow it
      Ashton Street, Liverpool, L69 3DA, GB
      53.418074 -2.967913
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