Name
Abstract | ||
|---|---|---|
We describe the origins of this project, its aims and its relevance to e-Science research. Particle physicists at the University of Manchester with experience of artificial neural networks (ANNs) have collaborated with clinicians at the University of Dundee to produce an ANN that is intended to predict survival rates and to indicate management profiles for cancer patients. Comparisons are made between typical data handling problems in particle physics and health care. The problems associated with data procurement, namely reliability and censoring are described, together with a discussion of how these problems were addressed. The inputs to the ANN and its decision output are discussed. The reliability of the ANN is assessed quantitatively. The prototype secure Web-based interface, which allows clinicians to input new patient data to the central node at the University of Manchester and to obtain prognoses from anywhere in the world is presented. For each topic, the e-Science relevance is described and underlined. Copyright (c) 2006 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1002/cpe.1045 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
cancer management, artificial neural network, e-Science | Journal | 19 |
Issue | ISSN | Citations |
2 | 1532-0626 | 2 |
PageRank | References | Authors |
0.42 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergey Dolgobrodov | 1 | 2 | 1.10 |
| Robin Marshall | 2 | 2 | 0.76 |
| Peter Moore | 3 | 2 | 0.76 |
| Rachel Bittern | 4 | 2 | 0.76 |
| Robert Steele | 5 | 5 | 2.57 |
| Alfred Cuschieri | 6 | 9 | 3.45 |