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Outcome Prediction for Cancer Patients
The book is organized into 4 distinct sections, each exploring the question of outcome prediction in cancer from a different perspective. The clinical problem and the challenges faced by clinicians in dealing with cancer are described in the first section. This includes discussions on TNM staging, accepted methods for survival analysis, and competing risks. The second section delves into the biological and genetic markers, as well as the role of bioinformatics. Understanding the genetic and environmental basis of cancers will aid in identifying high-risk populations and developing effective prevention and early detection strategies. The third section provides technical details of the mathematical analysis behind survival prediction, supported by examples from various types of cancers. The fourth section focuses on a number of machine learning methods that have been applied to decision support in cancer. The final section addresses how information is shared within the scientific and medical communities, as well as with the general population, using information technology and the World Wide Web.
The book covers applications for 8 types of cancer, including brain, eye, mouth, head and neck, breast, lungs, colon, and prostate, with contributions from authors in 5 different disciplines. This comprehensive resource serves as a valuable educational tool for medical informatics.
product information:
| Attribute | Value |
|---|---|
| publisher | Elsevier Science; 1st edition (November 28, 2006) |
| publication_date | November 28, 2006 |
| language | English |
| file_size | 14480 KB |
| text_to_speech | Enabled |
| screen_reader | Supported |
| enhanced_typesetting | Enabled |
| x_ray | Not Enabled |
| word_wise | Not Enabled |
| sticky_notes | On Kindle Scribe |
| print_length | 762 pages |
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