Decision Support System for Diagnosis and Treatment of Hearing Disorders : The Case of Tinnitus.

Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Objective -- 1.2 Organization of this Book -- 2 Tinnitus Treatment as a Problem Area -- 2.1 Tinnitus -- 2.1.1 Problem Description -- 2.1.2 Medical Background -- 2.2 Tinnitus Retraining Therapy -- 2.2.1 Neurophysiological Model -- 2.2.2 Habituation -- 2.3 Treatment Protocol -- 2.3.1 Patient Categories -- 2.4 Motivation for RS Project -- 2.4.1 Treatment Results -- 2.4.2 Patient Dataset -- 2.4.3 Problems with Human Approach -- 3 Recommender Solutions Overview -- 3.1 Recommender Systems Concept -- 3.1.1 Health Recommender Systems -- 3.2 Collaborative Recommendation -- 3.2.1 Simple Example -- 3.2.2 Example Applications -- 3.3 Content-Based Recommendation -- 3.3.1 High-Level Architecture -- 3.3.2 Content Representation and Recommender Techniques -- 3.3.3 Example Applications -- 3.4 Knowledge-Based Recommendation -- 3.4.1 Knowledge Representation and Reasoning -- 3.4.2 Example Applications -- 3.5 Hybrid Recommender Systems -- 3.5.1 Example Applications -- 3.6 Discussion -- 4 Knowledge Discovery Approach for Recommendation -- 4.1 Basic Concepts -- 4.1.1 Information Systems -- 4.1.2 Decision Tables -- 4.1.3 Reducts -- 4.2 Decision Rules -- 4.3 Classification Rules -- 4.4 Action Rules -- 4.4.1 Definitions -- 4.4.2 Algorithms -- 4.5 Meta Actions -- 4.5.1 Definition -- 4.5.2 Discovery Methods -- 4.6 Advanced Clustering Techniques -- 4.7 Conclusion -- 5 RECTIN System Design -- 5.1 System Analysis -- 5.2 System Architecture -- 5.2.1 Knowledge Base -- 5.2.2 Classification Module -- 5.2.3 Action Rules Module -- 5.3 Knowledge Engineering -- 5.3.1 Raw Data -- 5.3.2 Data Preprocessing -- 5.4 Summary -- 6 Experiment 1: Classifiers -- 6.1 Initial Feature Development -- 6.1.1 Tinnitus Background -- 6.1.2 Temporal Features for Tinnitus Induction -- 6.2 Preliminary Experiments -- 6.2.1 Assumptions.

6.2.2 Feature Selection -- 6.2.3 Results -- 6.2.4 Discussion -- 6.3 Second Experimental Setup -- 6.3.1 Pharmacology Data Analysis -- 6.3.2 Pivotal Features Development -- 6.3.3 Experiment Results -- 6.4 One-Patient-One-Tuple Experiment -- 6.5 Summary of Classification Experiments -- 6.5.1 Final Classifier Choice -- 7 Experiment 2: Diagnostic Rules -- 7.1 Methodology -- 7.1.1 Data Source -- 7.1.2 Attributes -- 7.1.3 Tasks Definition -- 7.2 Results -- 7.2.1 Interview -3mu Category -- 7.2.2 Audiology -3mu Category -- 7.2.3 Demographics -3mu Category -- 7.2.4 Pharmacology -3mu Category -- 7.2.5 Age -3mu Diseases -- 7.2.6 Pharmacology -3mu Tinnitus -- 7.2.7 Comprehensive Decision Rules -- 7.3 Conclusions -- 8 Experiment 3: Treatment Rules -- 8.1 Methodology -- 8.1.1 Task Definition -- 8.1.2 Decision Attribute Analysis -- 8.1.3 Temporal Feature Development -- 8.1.4 Imputation of Missing Features -- 8.1.5 Experimental Setup with New Attributes -- 8.2 Results -- 8.2.1 Treatment Protocol -- 8.2.2 Instrument Fitting -- 8.2.3 Treatment Personalized for Tinnitus Induction -- 8.2.4 Treatment Personalized for Medical Condition -- 8.3 Meta Actions Discovery Experiment -- 8.3.1 Output -- 8.4 Discussion -- 8.4.1 Advantages -- 8.4.2 Flaws -- 8.4.3 Algorithm Reexamined -- 9 Experiment 4: Treatment Rules Enhancement -- 9.1 Methodology -- 9.1.1 New Temporal Feature Development -- 9.1.2 Experimental Setup -- 9.2 Results -- 9.2.1 Instrument Fitting -- 9.2.2 Treatment Protocol -- 9.2.3 Treatment Personalized for Demographics -- 9.2.4 Treatment Personalized for Tinnitus Background -- 9.2.5 Treatment Personalized for Medical Condition -- 9.2.6 Meta Actions -- 9.3 Summary of Experiments on Rules Extraction -- 10 RECTIN Implementation -- 10.1 Application -- 10.2 Transactional Database -- 10.3 Classification Module -- 10.4 Rule Engine -- 10.4.1 Rete Algorithm for Rule Execution.

10.5 Conclusion -- 11 Final Conclusions and Future Work -- 11.1 Objective Verification -- 11.2 Further Work -- Appendix A Tinnitus Initial Interview Form -- Appendix B Tinnitus Follow-up Interview Form -- Appendix C Tinnitus Handicap Inventory -- References.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.