Ontologies in the Health Field

Ontologies in the Health Field

Michel Simonet (Laboratoire TIMC-IMAG and Institut de l’Ingénierie et de l’Information de Santé, France), Radja Messai (Laboratoire TIMC-IMAG and Institut de l’Ingénierie et de l’Information de Santé, France) and Gayo Diallo (Laboratoire TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France)
DOI: 10.4018/978-1-60566-218-3.ch002
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Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the Semantic Web. Although there is no consensus on a common definition of an ontology, it is necessary to understand their main features to be able to use them in a pertinent and efficient manner for data mining purposes. This chapter introduces the basic notions about ontologies, presents a survey of their use in medicine and explores some related issues: knowledge bases, terminology, and information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies.
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Ontologies have become a privileged and almost unavoidable means to represent and exploit knowledge and data. This is true in many domains, and particularly in the health field. Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the semantic web. In the health field, there are still more than one hundred classifications (e.g., ICD10, MeSH, SNOMED), which makes it very difficult to exploit data coded according to one or the other, or several of these classifications. The UMLS (Unified Medical Language System) initiative tries to provide a unified access to these classifications, in the absence of an ontology of the whole medical domain - still to come.

In order to apprehend the interest of ontologies in data mining, especially in the health domain, it is necessary to have a clear view of what an ontology is. Unfortunately, there is no consensus within the scientific community on a common definition of an ontology, which is somewhat paradoxical, as one of the characteristics of an ontology is to represent a consensus of a community on a given domain. However, one does not need to enter the specialists’ debate on ontologies to understand their main characteristics and therefore be able to use them in a pertinent and efficient manner for data mining purposes.

On a first level, one can think of an ontology as a means to name and structure the content of a domain. Among the numerous definitions that have been given, there is some kind of agreement that an ontology represents the concepts of a domain, the relationships between these concepts (IS-A and other relationships), the vocabulary used to designate them, and their definition (informal and/or formal). The IS-A relationship plays a central role, as it provides the (tree-like) skeleton of an ontology. This structure need not be a tree, as a concept may specialize several upper concepts, contrary to a taxonomy. Compared with a thesaurus, an ontology is freed from a particular language: an ontology deals with concepts, independently from the (natural) language that is used to designate them, while a thesaurus deals with terms that are expressed in a particular language. Moreover, a thesaurus does not enable the creation of new relationships between terms, whereas ontologies do.

There is no strict boundary between taxonomies, thesauri and ontologies, and a taxonomy may be considered as a particular case of an ontology. In practice, most ontologies rely on a taxonomic skeleton which is enriched with ontology-specific features. One can also notice that the conceptual schema of a database, expressed in object form, is close to an ontology (a micro-ontology) of the application domain of the database. Therefore, most people dealing with health data have been dealing with ontologies, either explicitly or implicitly – most often implicitly. However, making explicit the notion of ontology has made it possible to formalize and unite various formalisms and practices. The current ontology standard in the web universe, namely OWL1, might not be the final standard for ontologies, but it has initiated a movement towards the need for an agreement for such a standard.

Ontologies have their roots in Aristotle’s categories, and particularly in Porphyry’s tree-like representation (3rd century), which laid the foundations for modern ontologies. This tree-like structure is still present in ontologies and in most knowledge representation systems through the IS-A relationship. The attributes in object or frame-based systems and the roles in Description Logics provide the other relationships of a possibly corresponding ontology. However, the introduction of ontologies in the field of Computer Science by Gruber in the 90’s was not motivated by philosophical considerations but by the need of a representation in first-order logic of knowledge-based systems in order to facilitate their interoperability (Gruber, 1991). Today’s ontologies are still strongly linked to first-order logic, either through Description Logics, which constitute the main stream in the ontology domain, or through conceptual graphs, which also have a strong logic background. Ontologies have also become an unavoidable support to knowledge and data integration.

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Table of Contents
Riccardo Bellazzi
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Chapter 1
Jana Zvárová, Arnošt Veselý
This chapter introduces the basic concepts of medical informatics: data, information, and knowledge. Data are classified into various types and... Sample PDF
Data, Information and Knowledge
Chapter 2
Michel Simonet, Radja Messai, Gayo Diallo
Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status... Sample PDF
Ontologies in the Health Field
Chapter 3
Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to... Sample PDF
Cost-Sensitive Learning in Medicine
Chapter 4
Arnošt Veselý
This chapter deals with applications of artificial neural networks in classification and regression problems. Based on theoretical analysis it... Sample PDF
Classification and Prediction with Neural Networks
Chapter 5
Patrik Eklund, Lena Kallin Westin
Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data... Sample PDF
Preprocessing Perceptrons and Multivariate Decision Limits
Chapter 6
Xiu Ying Wang, Dagan Feng
The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new... Sample PDF
Image Registration for Biomedical Information Integration
Chapter 7
ECG Processing  (pages 137-160)
Lenka Lhotská, Václav Chudácek, Michal Huptych
This chapter describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG)... Sample PDF
ECG Processing
Chapter 8
EEG Data Mining Using PCA  (pages 161-180)
Lenka Lhotská, Vladimír Krajca, Jitka Mohylová, Svojmil Petránek, Václav Gerla
This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing.... Sample PDF
EEG Data Mining Using PCA
Chapter 9
Darryl N. Davis, Thuy T.T. Nguyen
Risk prediction models are of great interest to clinicians. They offer an explicit and repeatable means to aide the selection, from a general... Sample PDF
Generating and Verifying Risk Prediction Models using Data Mining
Chapter 10
Vangelis Karkaletsis, Konstantinos Stamatakis, Karampiperis, Karampiperis, Pythagoras Karampiperis, Pythagoras Karampiperis
The World Wide Web is an important channel of information exchange in many domains, including the medical one. The ever increasing amount of freely... Sample PDF
Management of Medical Website Quality Labels via Web Mining
Chapter 11
Rainer Schmidt
In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal... Sample PDF
Two Case-Based Systems for Explaining Exceptions in Medicine
Chapter 12
Bruno Crémilleux, Arnaud Soulet, Jiri Kléma, Céline Hébert, Olivier Gandrillon
The discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on... Sample PDF
Discovering Knowledge from Local Patterns in SAGE Data
Chapter 13
Jirí Kléma, Filip Železný, Igor Trajkovski, Filip Karel, Bruno Crémilleux
This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several... Sample PDF
Gene Expression Mining Guided by Background Knowledge
Chapter 14
Pamela L. Thompson, Xin Zhang, Wenxin Jiang, Zbigniew W. Ras, Pawel Jastreboff
This chapter describes the process used to mine a database containing data, related to patient visits during Tinnitus Retraining Therapy. The... Sample PDF
Mining Tinnitus Database for Knowledge
Chapter 15
Dinora A. Morales, Endika Bengoetxea, Pedro Larrañaga
Infertility is currently considered an important social problem that has been subject to special interest by medical doctors and biologists. Due to... Sample PDF
Gaussian-Stacking Multiclassifiers for Human Embryo Selection
Chapter 16
Mining Tuberculosis Data  (pages 332-349)
Marisa A. Sánchez, Sonia Uremovich, Pablo Acrogliano
This chapter reviews the current policies of tuberculosis control programs for the diagnosis of tuberculosis. The international standard for... Sample PDF
Mining Tuberculosis Data
Chapter 17
Mila Kwiatkowska, M. Stella Atkins, Les Matthews, Najib T. Ayas, C. Frank Ryan
This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules.... Sample PDF
Knowledge-Based Induction of Clinical Prediction Rules
Chapter 18
Petr Berka, Jan Rauch, Marie Tomecková
The aim of this chapter is to describe goals, current results, and further plans of long-time activity concerning application of data mining and... Sample PDF
Data Mining in Atherosclerosis Risk Factor Data
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