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What is Gradient Descent Method

Chapter 24
Handbook of Research on Distributed Medical Informatics and E-Health
Method for searching a local minimum of a function. In order to find a local minimum, the algorithm moves in the direction of the negative gradient (vector of the partial differentiations) of the function. In Case Based Reasoning it can be used to optimize the global similarity measure according to the preferences of the user (Stahl, 2003, pp. 105ff). For this purporse an error function must be defined by comparing the order of the cases preferred by the user and the order calculated by the global similarity measure. The gradient descent method searches the minimum of this error function.
Published in Chapter:
Case Based Reasoning for Customizing Treatment Processes
Carolin Kaiser (University of Erlangen-Nuremberg, Germany)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-002-8.ch024
Abstract
This chapter introduces a case based reasoning (CBR) system for customizing treatment processes. The CBR system enables the generating of inpatient and outpatient treatment processes and the supporting of e-services in heath care networks individually customized to the patients’ needs. According to the CBR paradigm, which solves problems based on past experience, the proposed system uses old treatment processes of similar former patients and modifies them for new patients. In general, CBR is an established and well suited artificial intelligence method to support medical decision making. However, CBR systems capable of planning treatment processes by adapting old treatment processes to fit new patients are rare. The aim of this system is to increase the treatment quality of the patient by providing physicians with valuable treatment propositions and to contribute to the development of medical CBR Systems by introducing procedures enabling the generating of new treatment processes by modifying former treatment processes.
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