Munro, P and Sanguansintukual, S
(2002)
A neural network approach to treatment optimization.
Proceedings / AMIA ... Annual Symposium. AMIA Symposium.
548 - 551.
ISSN 1531-605X
Abstract
Typical medical diagnosis applications of neural networks for prediction and classification require training data (observations) that include the "correct" category for a number of patient records. In this paper, we borrow a technique from control systems applications of neural networks. Optimal control parameters of a system are typically not known. Instead, we only know the effect on a remote system. The correct control action drives the remote system optimally. The learning technique requires two networks: one to model the system to be controlled (here, the patient), and one to optimize the treatment (here, the treating physician). The concept was tested with artificially generated noisy data, and gives promising results.
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Details
| Item Type: |
Article
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| Status: |
Published |
| Creators/Authors: |
| Creators | Email | Pitt Username | ORCID  |
|---|
| Munro, P | pwm@pitt.edu | PWM | | | Sanguansintukual, S | | | |
|
| Date: |
1 January 2002 |
| Date Type: |
Publication |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Journal or Publication Title: |
Proceedings / AMIA ... Annual Symposium. AMIA Symposium |
| Page Range: |
548 - 551 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Information Sciences > Information Science |
| Refereed: |
Yes |
| ISSN: |
1531-605X |
| Date Deposited: |
03 Jul 2013 14:25 |
| Last Modified: |
04 Feb 2019 15:59 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/19162 |
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