Data mining and medical knowledge management cases and applications

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Current research directions are looking at Data Mining (DM) and Knowledge Management (KM) as complementary and interrelated felds, aimed at supporting, with algorithms and tools, the lifecycle of knowledge, including its discovery, formalization, retrieval, reuse, and update. While DM focuses on Data Mining and Medical Knowledge Management: Cases and Applications Petr Berka University of Economics, Prague, Czech Republic Jan Rauch University of Economics, Prague, Czech Republic Djamel Abdelkader Zighed University of Lumiere Lyon 2, France Hershey • New York Medical inforMation science reference Director of Editorial Content: Kristin Klinger Managing Editor: Jamie Snavely Assistant Managing Editor: Carole Coulson Typesetter: Sean Woznicki Cover Design: Lisa Tosheff Printed at: Yurchak Printing Inc. Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue, Suite 200 Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com/reference and in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: http://www.eurospanbookstore.com Copyright © 2009 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identication purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Data mining and medical knowledge management : cases and applications / Petr Berka, Jan Rauch, and Djamel Abdelkader Zighed, editors. p. ; cm. Includes bibliographical references and index. Summary: "This book presents 20 case studies on applications of various modern data mining methods in several important areas of medi- cine, covering classical data mining methods, elaborated approaches related to mining in EEG and ECG data, and methods related to mining in genetic data"--Provided by publisher. ISBN 978-1-60566-218-3 (hardcover) 1. Medicine--Data processing--Case studies. 2. Data mining--Case studies. I. Berka, Petr. II. Rauch, Jan. III. Zighed, Djamel A., 1955- [DNLM: 1. Medical Informatics--methods--Case Reports. 2. Computational Biology--methods--Case Reports. 3. Information Storage and Retrieval--methods--Case Reports. 4. Risk Assessment--Case Reports. W 26.5 D2314 2009] R858.D33 2009 610.0285--dc22 2008028366 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating the library's complimentary electronic access to this publication. Editorial Advisory Board Riccardo Bellazzi, University of Pavia, Italy Radim Jiroušek, Academy of Sciences, Prague, Czech Republic Katharina Morik, University of Dortmund, Germany Ján Paralič, Technical University, Košice, Slovak Republic Luis Torgo, LIAAD-INESC Porto LA, Portugal Blaž Župan, University of Ljubljana, Slovenia List of Reviewers Ricardo Bellazzi, University of Pavia, Italy Petr Berka, University of Economics, Prague, Czech Republic Bruno Crémilleux, University Caen, France Peter Eklund, Umeå University, Umeå, Sveden Radim Jiroušek, Academy of Sciences, Prague, Czech Republic Jiří Kléma, Czech Technical University, Prague, Czech Republic Mila Kwiatkovska, Thompson Rivers University, Kamloops, Canada Martin Labský, University of Economics, Prague, Czech Republic Lenka Lhotská, Czech Technical University, Prague, Czech Republic Ján Paralić, Technical University, Kosice, Slovak Republic Vincent Pisetta, University Lyon 2, France Simon Marcellin, University Lyon 2, France Jan Rauch, University of Economics, Prague, Czech Republic Marisa Sánchez, National University, Bahía Blanca, Argentina Ahmed-El Sayed, University Lyon 2, France Olga Štěpánková, Czech Technical University, Prague, Czech Republic Vojtěch Svátek, University of Economics, Prague, Czech Republic Arnošt Veselý, Czech University of Life Sciences, Prague, Czech Republic Djamel Zighed, University Lyon 2, France Foreword xiv Preface xix Acknowledgment .xxiii Section I Theoretical Aspects Chapter I Data, Information and Knowledge 1 Jana Zvárová, Institute of Computer Science of the Academy of Sciences of the Czech R ep ublic v.v.i., Czech Republic; Center of Biomedical Informatics, Czech Republic Arnošt Veselý, Institute of Computer Science of the Academy of Sciences of the Czech Republic v.v.i., Czech Republic; Czech University of Life Sciences, Czech Republic Igor V ajda, Institutes of Computer Science and Information Theory and Automation of the Academy of Sciences of the Czech Republic v.v.i., Czech Republic Chapter II Ontologies in the Health Field 37 Michel Simonet, Laboratoire TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Radja Messai, Laboratoir e TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Gayo Diallo, Laboratoir e TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Ana Simonet, Laboratoir e TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Chapter III Cost-Sensitive Learning in Medicine 57 Alberto Freitas, University of Porto, Portugal; CINTESIS, Portugal Pavel Brazdil, LIAAD - INESC Porto L.A., Portugal; University of Porto, Portugal Altamiro Costa-Pereira, University of Porto, Portugal; CINTESIS, Portugal Table of Contents Chapter IV Classication and Prediction with Neural Networks 76 Arnošt Veselý, Czech University of Life Sciences, Czech Republic Chapter V Preprocessing Perceptrons and Multivariate Decision Limits 108 Patrik Eklund, Umeå University, Sweden Lena Kallin W estin, Umeå University, Sweden Section II General Applications Chapter VI Image Registration for Biomedical Information Integration 122 Xiu Ying Wang, BMIT Research Group, The University of Sydney, Australia Dag an Feng, BMIT Research Group, The University of Sydney, Australia; Hong Kong Polytechnic University, Hong Kong Chapter VII ECG Processing 137 Lenka Lhotská, Czech Technical University in Prague, Czech Republic  Václav Chudáček,CzechTechnicalUniversityinPrague,CzechRepublic Michal Huptych, Czech Technical University in Prague, Czech Republic Chapter VIII EEG Data Mining Using PCA 161 Lenka Lhotská, Czech Technical University in Prague, Czech Republic  Vladimír Krajča,FacultyHospitalNaBulovce,CzechRepublic Jitka Mohylová, Technical University Ostrava, Czech Republic Svojmil Petránek, Faculty Hospital Na Bulovce, Czech Republic Václav Gerla, Czech Technical University in Prague, Czech Republic Chapter IX Generating and Verifying Risk Prediction Models Using Data Mining . 181 Darryl N. Davis, University of Hull, UK Thuy T.T. Nguyen, University of Hull, UK Chapter X Management of Medical Website Quality Labels via Web Mining 206 V angelisKarkaletsis,NationalCenterofScienti.c Research“Demokritos”,Greece  Konstantinos Stamatakis,NationalCenterofScienticResearch“Demokritos”,Greece  PythagorasKarampiperis,NationalCenterofScienticResearch“Demokritos”,Greece Martin Labský, University of Economics, Prague, Czech Republic  MarekRůžička,UniversityofEconomics,Prague,CzechRepublic  VojtěchSvátek,UniversityofEconomics,Prague,CzechRepublic Enrique Amigó Cabrera, ETSI Informática, UNED, Spain Matti Pöllä, Helsinki University of Technology, Finland Miquel Angel Mayer, Medical Association of Barcelona (COMB), Spain Dagmar Villarroel Gonzales, Agency for Quality in Medicine (AquMed), Germany Chapter XI Two Case-Based Systems for Explaining Exceptions in Medicine 227 Rainer Schmidt, University of Rostock, Germany Section III Speci.c Cases Chapter XII Discovering Knowledge from Local Patterns in SAGE Data . 251 Bruno Crémilleux, Université de Caen, France Arnaud Soulet, Université François Rabelais de T ours, France  Jiří Kléma,CzechTechnicalUniversity,inPrague,CzechRepublic Céline Hébert, Université de Caen, France Olivier Gandrillon, Université de Lyon, France Chapter XIII Gene Expression Mining Guided by Background Knowledge . 268 JiříKléma, CzechTechnicalUniversityinPrague,CzechRepublic  FilipŽelezný,CzechTechnicalUniversityinPrague,CzechRepublic  IgorTrajkovski,JožefStefanInstitute,Slovenia Filip Karel, Czech Technical University in Prague, Czech Republic Bruno Crémilleux, Université de Caen, France Jakub Tolar, University of Minnesota, USA Chapter XIV Mining Tinnitus Database for Knowledge 293 Pamela L. Thompson, University of North Carolina at Charlotte, USA Xin Zhang, University of North Carolina at Pembroke, USA W enxin Jiang, University of North Carolina at Charlotte, USA Zbigniew W. Ras, University of North Carolina at Charlotte, USA Pawel Jastreboff, Emory University School of Medicine, USA Chapter XV Gaussian-Stacking Multiclassiers for Human Embryo Selection . 307 Dinora A. Morales, University of the Basque Country, Spain Endika Bengoetxea, University of the Basque Country , Spain Pedro Larrañaga, Universidad Politécnica de Madrid, Spain Chapter XVI Mining Tuberculosis Data . 332 Marisa A. Sánchez, Universidad Nacional del Sur, Argentina Sonia Ur emovich, Universidad Nacional del Sur, Argentina Pablo Acrogliano, Hospital Interzonal Dr. José Penna, Argentina Chapter XVII Knowledge-Based Induction of Clinical Prediction Rules . 350 Mila Kwiatkowska, Thompson Rivers University, Canada M. Stella Atkins, Simon Fraser University, Canada Les Matthews, Thompson Rivers University , Canada Najib T. Ayas, University of British Columbia, Canada C. Frank Ryan, University of British Columbia, Canada Chapter XVIII Data Mining in Atherosclerosis Risk Factor Data 376 Petr Berka, University of Economics, Prague, Czech Republic; Academy of Sciences of the Czech Republic, Prague, Czech Republic Jan Rauch, University of Economics, Praague, Czech Republic; Academy of Sciences of the Czech Republic, Prague, Czech Republic  Marie Tomečková,AcademyofSciencesoftheCzechRepublic,Prague,CzechRepublic Compilation of References . 398 About the Contributors 426 Index . 437 Foreword xiv Preface xix Acknowledgment .xxiii Section I Theoretical Aspects This section provides a theoretical and methodological background for the remaining parts of the book. It denes and explains basic notions of data mining and knowledge management, and discusses some general methods. Chapter I Data, Information and Knowledge 1 Jana Zvárová, Institute of Computer Science of the Academy of Sciences of the Czech R ep ublic v.v.i., Czech Republic; Center of Biomedical Informatics, Czech Republic Arnošt Veselý, Institute of Computer Science of the Academy of Sciences of the Czech Republic v.v.i., Czech Republic; Czech University of Life Sciences, Czech Republic Igor V ajda, Institutes of Computer Science and Information Theory and Automation of the Academy of Sciences of the Czech Republic v.v.i., Czech Republic This chapter introduces the basic concepts of medical informatics: data, information, and knowledge. It shows how these concepts are interrelated and can be used for decision support in medicine. All discussed approaches are illustrated on one simple medical example. Chapter II Ontologies in the Health Field 37 Michel Simonet, Laboratoire TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Radja Messai, Laboratoire TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Gayo Diallo, Laboratoir e TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Ana Simonet, Laboratoir e TIMC-IMAG, Institut de l’Ingénierie et de l’Information de Santé, France Detailed Table of Contents This chapter introduces the basic notions of ontologies, presents a survey of their use in medicine, and explores some related issues: knowledge bases, terminology, information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies. Chapter III Cost-Sensitive Learning in Medicine 57 Alberto Freitas, University of Porto, Portugal; CINTESIS, Portugal Pavel Brazdil, LIAAD - INESC Porto L.A., Portugal; University of Porto, Portugal Altamir o Costa-Pereira, University of Porto, Portugal; CINTESIS, Portugal Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specic diagnostic test) and misclassication costs (e.g. the cost of a false negative test). This chapter presents some concepts related to cost-sensitive learning and cost-sensitive classication in medicine and reviews research in this area. Chapter IV Classication and Prediction with Neural Networks 76 Arnošt Veselý, Czech University of Life Sciences, Czech Republic This chapter describes the theoretical background of articial neural networks (architectures, methods of learning) and shows how these networks can be used in medical domain to solve various classica- tion and regression problems. Chapter V Preprocessing Perceptrons and Multivariate Decision Limits 108 Patrik Eklund, Umeå University, Sweden Lena Kallin W estin, Umeå University, Sweden This chapter introduces classication networks composed of preprocessing layers and classication networks, and compares them with “classical” multilayer percpetrons on three medical case studies. Section II General Applications This section presents work that is general in the sense of a variety of methods or variety of problems described in each of the chapters. Chapter VI Image Registration for Biomedical Information Integration 122 Xiu Ying Wang, BMIT Research Group, The University of Sydney, Australia Dag an Feng, BMIT Research Group, The University of Sydney, Australia; Hong Kong Polytechnic University, Hong Kong [...]... available, or background knowledge, (BK) is exploited to drive data gathering and experimental planning, and to structure the databases and data warehouses BK is used to properly select the data, choose the data mining strategies, improve the data mining algorithms, and finally evaluates the data mining results (Bellazzi, Zupan, 2008; Bellazzi, Zupan, 2008) The output of the data analysis process is... 2005) and the Rhene systems in Italy (Montani et al., 2006) Moreover, several commercial solutions for the joint management of information, data, and knowledge are available on the market It is almost inevitable that in the near future, DM and KM technologies will be an essential part of hospital and research information systems The book Data Mining and Medical Knowledge Management: Cases and Applications ... book Data Mining and Medical Knowledge Management: Cases and Applications is knowledge A number of definitions of this notion can be found in the literature: • • • • Knowledge is the sum of what is known: the body of truth, information, and principles acquired by mankind Knowledge is human expertise stored in a person’s mind, gained through experience, and interaction with the person’s environment Knowledge. .. analysis of data stored in their databases It results into numerous applications of various data mining tools and techniques The analyzed data are in different forms covering simple data matrices, complex relational databases, pictorial material, time series, and so forth Efficient analysis requires knowledge not only of data analysis techniques but also involvement of medical knowledge and close cooperation... spectrum of applications of data mining and knowledge management in medical area The book is divided into 3 sections The first section entitled “Theoretical Aspects” discusses some basic notions of data mining and knowledge management with respect to the medical area This section presents a theoretical background for the rest of the book Chapter I introduces the basic concepts of medical informatics: data, ... notion of knowledge (in the domain of medicine) from two different points of view: data mining and knowledge management Knowledge Management (KM) comprises a range of practices used by organizations to identify, create, represent, and distribute knowledge Knowledge Management may be viewed from each of the following perspectives: • • Techno-centric: A focus on technology, ideally those that enhance knowledge. .. molecules), and textual data (e.g interviews with patients, physician’s notes) Thus there is a need for efficient mining in images, graphs, and text, which is more difficult than mining in “classical” relational databases containing only numeric or categorical attributes Another important issue in mining medical data is privacy and security; medical data are collected on patients, misuse of these data or... real-world databases requires a broad scope of techniques and forms of knowledge Both the knowledge and the applied methods should fit the discovery tasks and should adapt to knowledge hidden in the data Knowledge discovery has been successfully used in various application areas: business and finance, insurance, telecommunication, chemistry, sociology, or medicine Data mining in biology and medicine... source data, whether completely or at least partly Due to the importance of data and processing thereof in the information age we live in, as well as the attention both theory and practice of handling data receives, we can say that a new field is being born, called data engineering One of the essential notions of data engineering is metadata It is data about data , i.e., a data description of other data. .. the basic concepts of medical informatics: data, information, and knowledge Data are classified into various types and illustrated by concrete medical examples The concept of knowledge is formalized in the framework of a language related to objects, properties, and relations within ontology Various aspects of knowledge are studied and illustrated on examples dealing with symptoms and diseases Several . Cataloging-in-Publication Data Data mining and medical knowledge management : cases and applications / Petr Berka, Jan Rauch, and Djamel Abdelkader Zighed,. essential part of hospital and research information systems. The book Data Mining and Medical Knowledge Management: Cases and Applications is a collec-
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