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
Resource Information
The item Outcome prediction in cancer, editors, Azzam F.G. Taktak and Anthony C. Fisher represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Outcome prediction in cancer, editors, Azzam F.G. Taktak and Anthony C. Fisher represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- 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)
- Language
- eng
- Extent
- xx, 461 p.
- 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
- Isbn
- 9780444528551
- Label
- Outcome prediction in cancer
- Title
- Outcome prediction in cancer
- Statement of responsibility
- editors, Azzam F.G. Taktak and Anthony C. Fisher
- 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
- 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
- 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.
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Outcome-prediction-in-cancer-editors-Azzam-F.G./a56x8vDckJw/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Outcome-prediction-in-cancer-editors-Azzam-F.G./a56x8vDckJw/">Outcome prediction in cancer, editors, Azzam F.G. Taktak and Anthony C. Fisher</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>
