Neural networks (NN) have become established as powerful tools for complex pattern recognition problems. One application which appears well suited to NN methods is the identification of prognostic groups, to be used for treatment planning. For many cancer, studies of cancer cell biology have added many factors of potential prognostic value, but the way in which these interact with known factors is generally not well studied. The potential of NNs to model these data in a non-linear fashion has only begun to be explored. NNs are not part of standard statistical packages, making them relatively inaccessible to many statisticians. More importantly, current NN methods cannot accommodate censored outcome variables. This proposal is for development of algorithms for censored-data NNs, implementation of these within a comprehensive statistical package, and evaluation of alternative approaches.
The aim i s to provide statisticians involved with clinical decision making with more ready access to NN technology, and with the means to analyze survival-type data. The value of NNs in this field cannot be addressed by any single investigator, but by providing the software that is needed, and some guidelines for its use, we anticipate that research in this field will be stimulated.

Proposed Commercial Applications

The power of NNs has been recognized in the marketplace, and NNs are widely used. However, there is a real need to make NN more accessible for clinical applications by incorporating an easy- to-use NN program, providing the most commonly needed NN models and functions, into a mainstream statistical package. Furthermore, none of the currently available packages addresses the specific problem of censored data; we expect to find an immediate market among statisticians dealing with clinical data.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Small Business Technology Transfer (STTR) Grants - Phase II (R42)
Project #
5R42NS033899-03
Application #
2460603
Study Section
Special Emphasis Panel (ZRG7-SSS-9 (01))
Program Officer
Nichols, Paul L
Project Start
1994-09-30
Project End
1999-07-31
Budget Start
1997-08-01
Budget End
1999-07-31
Support Year
3
Fiscal Year
1997
Total Cost
Indirect Cost
Name
Epicenter Software
Department
Type
DUNS #
City
Pasadena
State
CA
Country
United States
Zip Code
91106