DocumentCode
1915627
Title
Development of a neural network derived index for early detection of prostate cancer
Author
Zhang, Zhen ; Zhang, Hong
Author_Institution
Med. Univ. of South Carolina, Charleston, SC, USA
Volume
5
fYear
1999
fDate
1999
Firstpage
3636
Abstract
ProstAsure is a neural network-derived algorithm which analyzes the profile of multiple serum tumor markers and produces a single-valued diagnostic index (ProstAsure Index, or PI) for early detection of prostate cancer (CaP). PI has been validated through multiple clinical studies with a fairly large number of blind independent test patients and has become the first of such tests commercially available through reference laboratories in the US and other countries as a clinical information processing service. We first describe briefly the development of the PI algorithm with a summary of clinical study results comparing PI with the currently accepted CaP detection tools. We then focus the discussion on two issues in developing a neural network-based clinical diagnostic system: 1) constructing training datasets under clinical constraints; and 2) estimating generalization performance by gauging the shape and “smoothness” of decision boundary surfaces of a derived classification system
Keywords
cancer; learning (artificial intelligence); medical diagnostic computing; multilayer perceptrons; patient diagnosis; pattern classification; clinical information processing service; decision boundary surfaces; generalization performance; multiple serum tumor markers; neural network derived index; neural network-based clinical diagnostic system; prostate cancer; single-valued diagnostic index; Cancer detection; Data preprocessing; Diseases; Input variables; Medical diagnostic imaging; Neoplasms; Neural networks; Prostate cancer; Support vector machines; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.836259
Filename
836259
Link To Document