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

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

Dimosthenis Kyriazis (National Technical University of Athens, Greece), Anastasios Doulamis (National Technical University of Athens, Greece) and Theodora Varvarigou (National Technical University of Athens, Greece)
DOI: 10.4018/978-1-60566-274-9.ch013
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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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Introduction

The rapid progress in publishing articles and the huge amount of data being stored, accessed and transmitted in the biological and medical domain has led to the advent of applications that perform Natural Language Processing (NLP)Natural Language Processing (NLP) in order to enable researchers, doctors and other actors in the aforementioned domain to search and retrieve the relevant content. In this context, the traditional approaches of searching, retrieving and organizing the medical data, using only text annotation, cannot describe the medical content with high efficiency. For this reason, several content-based retrieval mechanisms and approaches have been proposed, some of which work by extracting high level semantic features of the content.

Despite, however, the fact that semantic segmentation has attracted much attention recently, other features that describe the content such as keywords or categories are usually used for implementing content-based retrieval algorithms. To reduce the limitations emerged by using low-level descriptors and simultaneously to increase the performance of content-based algorithms, the human can be considered as a part of the retrieval process, in an interactive framework. This means that initially the user evaluates the results, provided by the system and then the system adapts its performance according to the user’s demands. In this framework, a feedback is established from the user to the system based on the most relevant articles, which is usually called relevance feedback. Such an approach, apart from eliminating the gap between high-level and low-level features, it also reduces the problems related to the subjectivity of humans, which often interpret the same medical content in a different way.

To address the content interpretation and classification, new adaptive and interactive management schemes should be introduced, which are capable of updating the system response with respect to the current user’s information needs and preferences. One way to achieve adaptability of the system response to the users’ needs is to modify the similarity measure used for ranking data. In this way, retrieval, organization and transmission of the information are updated in accordance with the humans’ perception of the content through a dynamic real time learning strategy based on the users’ interaction.

One of the interactive learning techniques is relevance feedbackrelevance feedback (originated from text-based information retrieval systems), which adapts the response of a system according to the relevant information feedback to it so that the adjusted response is a better approximation to the user’s information needs. Usually, relevant information is provided by the user in an interactive framework, who evaluates the results according to his/her demands and preferences. Relevance feedback has been widely used in text-based information retrieval systems (J. Rocchio, 1971). Although it is not restricted to description environments where similarity measures are used, in databases where similarity-based queries are applied (Y. Ishikawa, 1998), relevance feedback refers to the mechanism which updates the similarity measure with respect to the relevant/irrelevant information, as indicated by the user. Relevance feedback confronts the subjectivity of humans in perceiving medical content and also eliminates the gap between high-level semantics and low-level features, which are often used for content description and modeling (Y. Rui, 1998). The following figure (Figure 1) presents a block diagram of a relevance feedback scheme.

Figure 1.

Design of the relevance feedback mechanism for medical content retrieval systems

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Table of Contents
Preface
Violaine Prince, Mathieu Roche
Chapter 1
Sophia Ananiadou
Text mining provides the automated means to manage information overload and overlook. By adding meaning to text, text mining techniques produce a... Sample PDF
Text Mining for Biomedicine
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Chapter 2
Dimitrios Kokkinakis
The identification and mapping of terminology from large repositories of life science data onto concept hierarchies constitute an important initial... Sample PDF
Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH®
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Chapter 3
M. Teresa Martín-Valdivia, Arturo Montejo-Ráez, M. C. Díaz-Galiano, José M. Perea Ortega, L. Alfonso Ureña-López
This chapter argues for the integration of clinical knowledge extracted from medical ontologies in order to improve a Multi-Label Text... Sample PDF
Expanding Terms with Medical Ontologies to Improve a Multi-Label Text Categorization System
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Chapter 4
Piotr Pezik, Antonio Jimeno Yepes, Dietrich Rebholz-Schuhmann
The present chapter discusses the use of terminological resources for Information Retrieval in the biomedical domain. The authors first introduce a... Sample PDF
Using Biomedical Terminological Resources for Information Retrieval
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Chapter 5
Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet
The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a... Sample PDF
Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval
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Chapter 6
Vincent Claveau
This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This... Sample PDF
Translation of Biomedical Terms by Inferring Rewriting Rules
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Chapter 7
Nils Reiter, Paul Buitelaar
This chapter is concerned with lexical enrichment of ontologies, that is how to enrich a given ontology with lexical information derived from a... Sample PDF
Lexical Enrichment of Biomedical Ontologies
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Chapter 8
Torsten Schiemann, Ulf Leser, Jörg Hakenberg
Ambiguity is a common phenomenon in text, especially in the biomedical domain. For instance, it is frequently the case that a gene, a protein... Sample PDF
Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach
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Chapter 9
M. Narayanaswamy, K. E. Ravikumar, Z. Z. Hu, K. Vijay-Shanker, C. H. Wu
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Information Extraction of Protein Phosphorylation from Biomedical Literature
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Chapter 10
Yves Kodratoff, Jérôme Azé, Lise Fontaine
This chapter argues that in order to extract significant knowledge from masses of technical texts, it is necessary to provide the field specialists... Sample PDF
CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence
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Chapter 11
Yun Niu, Graeme Hirst
The task of question answering (QA) is to find an accurate and precise answer to a natural language question in some predefined text. Most existing... Sample PDF
Analyzing the Text of Clinical Literature for Question Answering
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Chapter 12
Nadine Lucas
This chapter presents the challenge of integrating knowledge at higher levels of discourse than the sentence, to avoid “missing the forest for the... Sample PDF
Discourse Processing for Text Mining
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Chapter 13
Dimosthenis Kyriazis, Anastasios Doulamis, Theodora Varvarigou
In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical... Sample PDF
A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems
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Chapter 14
Yitao Zhang, Jon Patrick
The fast growing content of online articles of clinical case studies provides a useful source for extracting domain-specific knowledge for improving... Sample PDF
Extracting Patient Case Profiles with Domain-Specific Semantic Categories
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Chapter 15
Laura I. Furlong, Ferran Sanz
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Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach
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Chapter 16
Francisco M. Couto, Mário J. Silva, Vivian Lee, Emily Dimmer, Evelyn Camon, Rolf Apweiler
Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a... Sample PDF
Verification of Uncurated Protein Annotations
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Chapter 17
Burr Settles
ABNER (A Biomedical Named Entity Recognizer) is an open-source software tool for text mining in the molecular biology literature. It processes... Sample PDF
A Software Tool for Biomedical Information Extraction (And Beyond)
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Chapter 18
Asanee Kawtrakul, Chaveevarn Pechsiri, Sachit Rajbhandari, Frederic Andres
Valuable knowledge has been distributed in heterogeneous formats on many different Web sites and other sources over the Internet. However, finding... Sample PDF
Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering
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Chapter 19
Christophe Jouis, Magali Roux-Rouquié, Jean-Gabriel Ganascia
Identical molecules could play different roles depending of the relations they may have with different partners embedded in different processes, at... Sample PDF
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Chapter 20
Jon Patrick, Pooyan Asgari
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About the Contributors