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2017 | Book

Introduction Read chapter Read first chapter

Decision Support System for Diagnosis and Treatment of Hearing Disorders

The Case of Tinnitus

Authors: Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff

Publisher: Springer International Publishing

Book Series : Studies in Computational Intelligence

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

The book presents a knowledge discovery based approach to build a recommender system supporting a physician in treating tinnitus patients with the highly successful method called Tinnitus Retraining Therapy.
It describes experiments on extracting novel knowledge from the historical dataset of patients treated by Dr. P. Jastreboff so that to better understand factors behind therapy's effectiveness and better personalize treatments for different profiles of patients.
The book is a response for a growing demand of an advanced data analytics in the healthcare industry in order to provide better care with the data driven decision-making solutions.
The potential economic benefits of applying computerized clinical decision support systems include not only improved efficiency in health care delivery (by reducing costs, improving quality of care and patient safety), but also enhancement in treatment's standardization, objectivity and availability in places of scarce expert's knowledge on this difficult to treat hearing disorder.
Furthermore, described approach could be used in assessment of the clinical effectiveness of evidence-based intervention of various proposed treatments for tinnitus.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
Recently, there has been an increasing interest in business analytics and big data tools to understand and drive industries evolution. The healthcare industry is also interested in new methods to analyze data and provide better care. Given the wealth of data that various institutions are accumulating, it is natural to take advantage of data driven decision-making solutions. Recommender systems proved to be a valuable mean to deal with the decision problems, especially in commercial merchandising. They are of special importance nowadays, when people are facing information overload and the growth and variety of information (products, news) available on the Web frequently overwhelms individuals. It leads them, in turn, to make poor decisions and decreases their well-being. Recommender systems enable automation of some of strategies in human decision making, support their users in various processes, providing advice that is both high-quality and high-personalized. In the area of healthcare they provide valuable support for physicians treating their patients, such as the one described in [Szl15]. The potential economic benefits of applying computerized clinical decision support systems include improved efficiency in health care delivery e.g. by reducing costs as well as improved quality of care and improved patient safety.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 2. Tinnitus Treatment as a Problem Area
Abstract
This chapter presents the decision problem area which will be supported with a recommender system technology, that is, tinnitus diagnosis and treatment. It will introduce the problem of tinnitus and next, the successful method of treatment applied by doctor P. Jastreboff. At the end of this chapter major results from the treatment will be showed, along with possible new challenges, which can be handled with the help of information technology.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 3. Recommender Solutions Overview
Abstract
This chapter aims at providing an overview of RS technology, describing different types of RS, with emphasis on choosing the right approach for the system supporting tinnitus treatment and justifying particular choice. Current generation of recommendation methods is presented in division to four main categories:
  • collaborative,
  • content-based,
  • knowledge-based,
  • hybrid.
The chapter introduces basic concepts of each type, along with their mathematical/algorithmic foundations and general system architectures. The last section compares these different approaches with regard to requirements for tinnitus therapy recommendation and provides motivation to choose the rule-based approach for building a recommender system for the given problem area.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 4. Knowledge Discovery Approach for Recommendation
Abstract
This chapter presents concepts of action rules, proposed by Ras and Wieczorkowska in 2000 [RW00] and meta-actions, as a proposed approach for building a rule-based (knowledge-based) recommender system for tinnitus treatment, and motivation for using such methods. It also presents theoretical foundations and algorithms for automatic action rules extraction, as methods for domain knowledge discovery.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 5. RECTIN System Design
Abstract
RECTIN system (shortcut for RECommender for TINnitus) is a prototyping method proposed within this work to verify the hypothesis of possibility to apply information technology in supporting physicians, dealing with tinnitus patients, in the diagnosis and treatment. This chapter describes major steps in the system design: analysis with main use cases for the system, deployment architecture, with detailed description of each component and implementation project, including transactional database and application. Also, knowledge engineering approach is presented, along with detailed description of raw dataset of tinnitus patients and visits, which was made available to the authors. This section also introduces approach taken to data preprocessing so that to make it useful for creating a knowledge base, on which data mining can be performed.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 6. Experiment 1: Classifiers
Abstract
Following the dataset preprocessing, the next step in implementing RECTIN is classification module development. The classification module will use a model built on historical patients’ data, in order to support physicians in suggesting optimal treatment approach for new patients. Categorization is rather easy and relatively broad. However, a specific approach within each category varies. Before implementing this module, it is necessary to extract new, useful features and conduct experiments in order to obtain the most accurate classifier on the prepared dataset. It is assumed to reiterate the step of feature development in order to obtain the best combination of feature extraction/selection method and the prediction method. This involves the calibration and tuning of prediction methods, as well as comparing them and evaluating in terms of accuracy, F-score and confusion matrix.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 7. Experiment 2: Diagnostic Rules
Abstract
Association rule tasks were defined in order to discover common patterns in patients’ visits dataset. Before defining data mining task on rule discovery, it is necessary to formulate an analytical problem in the first place. The main examined associations of interest would be factors affecting patient’s category. Such discovered association rules can be regarded as decision rules, supporting classifier developed in the previous step.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 8. Experiment 3: Treatment Rules
Abstract
Action rules should help in choosing treatment actions in the course of Tinnitus Retraining Therapy in subsequent visits. In order to understand the process of treatment and formulate appropriate data mining tasks in LISp-Miner, it is necessary to identify treatments actions taken by the doctor to improve tinnitus/hyperacusis patient’s condition.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 9. Experiment 4: Treatment Rules Enhancement
Abstract
Experiments on action rules, described in the previous section, did not consider temporal dependencies between patient’s visits (that is, at what relative point in timeline particular actions were taken). On the other hand, it would be effective to search for temporal dependencies between particular treatment actions and their observable results in the form of changed score denoting tinnitus severity. New approach should allow to assess treatment action effectiveness in temporal terms and consider their sequence. For example, some actions might take effect after some time elapse and not be effective in the short-term.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 10. RECTIN Implementation
Abstract
This chapter describes prototype RECTIN implementation with regard to each component. For classification module and rule engine module, the most interesting code parts are listed.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Chapter 11. Final Conclusions and Future Work
Abstract
This book presented a process of analysis, design and prototype implementation of RECTIN recommender system, as a solution to the problem of supporting tinnitus treatment based on Tinnitus Retraining Therapy in a medical facility. Proposed approach in supporting physicians’ diagnosis and treatment decisions addresses scarcity of expert knowledge, time restrictions in today’s medical practice and the need for more efficient evaluation of different treatment methods. Such system can provide accurate support at any time, with full consideration of individual patient profiles, including: demographics, medical history, and tinnitus background.
Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
Backmatter
Metadata
Title
Decision Support System for Diagnosis and Treatment of Hearing Disorders
Authors
Katarzyna A. Tarnowska
Zbigniew W. Ras
Pawel J. Jastreboff
Copyright Year
2017
Electronic ISBN
978-3-319-51463-5
Print ISBN
978-3-319-51462-8
DOI
https://doi.org/10.1007/978-3-319-51463-5

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