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The Value of Disproportionality Analysis Signal Detection Methods, the Development and Testing of Covariability Techniques, and the Importance of Ontology Pharmacovigilance Decision Support: The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology Gary W. Saunders Submitted in total ful¯llment of the requirements for the degree of Doctor of Philosophy University of Ballarat Ballarat, Victoria April 2006 °c Copyright by Gary W. Saunders, 2006 UNIVERSITY OF BALLARAT DEPARTMENT OF SCHOOL OF INFORMATION TECHNOLOGY AND MATHEMATICAL SCIENCES The undersigned hereby certify that they have read and recommend to the Faculty of Graduate Studies for acceptance a thesis entitled \Pharmacovigilance Decision Support: The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology" by Gary W. Saunders in total ful¯llment of the requirements for the degree of Doctor of Philosophy. Dated: April 2006 External Examiner: Manfred Hauben Research Supervisor: Alex Rubinov Examing Committee: Bill DuMouchel Panos Pardalos ii UNIVERSITY OF BALLARAT Date: April 2006 Author: Gary W. Saunders Title: Pharmacovigilance Decision Support: The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology Department: School of Information Technology and Mathematical Sciences Degree: Ph.D. Convocation: April Year: 2006 Permission is herewith granted to University of Ballarat to circulate and to have copied for non-commercial purposes, at its discretion, the above title upon the request of individuals or institutions. Signature of Author THE AUTHOR RESERVES OTHER PUBLICATION RIGHTS, AND NEITHER THE THESIS NOR EXTENSIVE EXTRACTS FROM IT MAY BE PRINTED OR OTHERWISE REPRODUCED WITHOUT THE AUTHOR'S WRITTEN PERMISSION. THE AUTHOR ATTESTS THAT PERMISSION HAS BEEN OBTAINED FOR THE USE OF ANY COPYRIGHTED MATERIAL APPEARING IN THIS THESIS (OTHER THAN BRIEF EXCERPTS REQUIRING ONLY PROPER ACKNOWLEDGEMENT IN SCHOLARLY WRITING) AND THAT ALL SUCH USE IS CLEARLY ACKNOWLEDGED. iii Table of Contents Table of Contents iv List of Tables xi List of Figures xv Abstract xvii Statement of Authorship xviii Acknowledgements xxi Introduction 1 0.1 Background . 1 0.2 The Inadequacy of Current Postmarketing Surveillance . 4 0.3 Research Questions . 6 0.4 Contribution . 6 1 Theoretic connections and Philosophic reflections for Adverse Drug Event Mon- itoring Systems 8 1.1 Introduction . 8 1.1.1 Philosophic Scope . 9 1.1.2 Overview . 10 1.2 Probability and Events . 10 1.2.1 Risk . 11 1.2.2 Rare Events . 13 1.3 Data Mining . 22 1.3.1 Association Rules . 25 1.4 Classi¯cation . 27 1.4.1 Classi¯cation in Data Mining . 28 1.5 Uncertainty and missing values . 29 1.6 Fuzzy logic . 30 1.6.1 Fuzzy truth values . 32 1.6.2 Fuzzy relations . 32 1.6.3 Fuzzy sets . 33 1.7 Rough set theory . 34 1.7.1 Rough Sets . 35 1.8 Soft Computing . 35 iv 1.9 Text categorization . 36 1.9.1 Linear classi¯cation methods . 37 1.9.2 Boosting . 38 1.9.3 Biological relationships . 40 1.9.4 Uncertainty . 41 1.9.5 Logistic regression . 41 1.9.6 Term Association . 42 1.9.7 Maximal Association Rules . 42 1.9.8 Thresholding . 43 1.9.9 Event Tracking . 43 1.9.10 Learning, cross-validation . 43 1.9.11 Hierarchical classi¯cation . 44 1.9.12 Semantic features and domain knowledge . 44 1.9.13 Text categorization { concluding remarks . 44 1.10 Ontology . 45 1.10.1 Background on ontology . 46 1.10.2 Ontology examples . 48 1.11 Conclusion . 52 1.11.1 Risk and rare events . 52 1.11.2 Data mining . 53 1.11.3 Fuzzy logic . 53 1.11.4 Text categorization . 54 1.11.5 Ontology . 54 2 Adverse Drug Events 55 2.1 Adverse Drug Reactions . 55 2.1.1 Automated ADR Signal Detection . 56 2.1.2 Reactions and Syndromes . 60 2.1.3 Predisposition . 61 2.1.4 Other Factors a®ecting ADRs { interactions . 63 2.2 Reaction and Drug Ontologies . 67 2.2.1 System organ class (SOC) system . 67 2.2.2 MeDRA system to be adopted by ADRAC . 67 2.2.3 Semantic Distance between ADR Terms . 69 2.2.4 Anatomical-Therapeutic-Chemical (ATC) system for drugs . 69 2.3 Treatment of Chronic Inflammatory Disease . 70 2.4 Further application of ADR Knowledge . 70 2.5 Methodology for Addressing the Research Questions . 71 2.6 Summary . 72 3 Application of Reaction and Drug Ontologies 74 3.1 Introduction . 74 3.2 Resources . 74 3.3 Reaction Terms . 74 3.3.1 Critical Terms . 75 3.4 System organ class . 75 3.4.1 Five cardiovascular groups { Card20 data set . 77 3.4.2 Haemic and lymphatic systems { Blood data set . 77 3.4.3 Nervous system and special senses { Neuro data set . 78 v 3.5 Drug Terms . 78 3.5.1 Suspect Codes . 78 3.6 Anatomical-Therapeutic-Chemical (ATC) system of drug classi¯cation . 78 3.6.1 Problems concerning the ATC currently . 79 3.7 ATC Classi¯cation of drugs reported in ADRAC . 79 3.7.1 Methods . 79 3.7.2 Results . 82 3.7.3 Discussion . 82 3.7.4 Concluding Remarks . 83 4 Preliminary Study of Cardiovascular Reactions in ADRAC Data 90 4.1 Introduction . 90 4.2 Methods . 92 4.3 Meta-SOC . 92 4.3.1 System Organ Class Codes in Meta Systems Organ Class Groups . 92 4.4 The cardiovascular group. The data set Card1 .................... 93 4.4.1 Analysis of some reactions . 94 4.4.2 Drugs associated with the Cardiovascular group . 97 4.5 The Data Set Card2 ................................... 100 4.6 The Fuzzy Classi¯cation Algorithm (QCA) . 101 4.6.1 Fuzzy Classi¯cation . 102 4.6.2 Potential Reactions . 104 4.6.3 Interaction of Drugs . 104 4.7 Card2: Results of numerical experiments obtained by QCA . 106 4.7.1 The Novelty Coe±cient . 107 4.7.2 Prediction of reactions by a given combination of drugs . 109 4.7.3 Potential reactions: the calculation of degrees for each drug using all data Card2 ....................................... 110 4.7.4 Interaction of Drugs . 110 4.8 The Algorithm FDM . ..
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