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A neural network approach to treatment optimization.

Munro, P and Sanguansintukual, S (2002) A neural network approach to treatment optimization. Proceedings / AMIA ... Annual Symposium. AMIA Symposium. 548 - 551. ISSN 1531-605X

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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
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Munro, Ppwm@pitt.eduPWM
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: 01 Nov 2017 14:02
URI: http://d-scholarship.pitt.edu/id/eprint/19162

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