A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems

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Author(s): Dimosthenis Kyriazis (National Technical University of Athens, Greece), Anastasios Doulamis (National Technical University of Athens, Athens, Greece)and Theodora Varvarigou (National Technical University of Athens, Greece)
Copyright: 2009
Pages: 18
Source title: Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration
Source Author(s)/Editor(s): Violaine Prince (University Montpellier 2, France)and Mathieu Roche (University Montpellier 2, France)
DOI: 10.4018/978-1-60566-274-9.ch013

Keywords: Bioinformatics / Health Information Systems / Medical Information Science Reference / Medicine & Healthcare

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Abstract

In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical content) retrieval systems. In greater detail, the user who searches for information is considered to be part of the retrieval process in an interactive framework, who evaluates the results provided by the system so that the user automatically updates its performance based on the users’ feedback. In order to achieve the latter, we propose an adaptively trained neural network (NN) architecture that is able to implement the non- linear feedback. The term “adaptively” refers to the functionality of the neural network to update its weights based on the user’s content selection and optimize its performance.

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A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems

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