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Decision Support System for Diagnosis and Treatment of Hearing Disorders: The Case of Tinnitus de Katarzyna A. Tarnowska

Decision Support System for Diagnosis and Treatment of Hearing Disorders: The Case of Tinnitus: Studies in Computational Intelligence, cartea 685

Autor Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
en Limba Engleză Hardback – 3 mar 2017
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.
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Specificații

ISBN-13: 9783319514628
ISBN-10: 3319514628
Pagini: 162
Ilustrații: X, 160 p. 33 illus.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.42 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Preface.- Introduction.- Tinnitus treatment as a problem area .- Recommender solutions overview.- Knowledge discovery approach for recommendation.- RECTIN system design.- Experiment 1: classifiers.- Experiment 2: diagnostic rules.- Experiment 3: treatment rules.- Experiment 4: treatment rules enhancement.- RECTIN implementation.- Final conclusions and future work.- References.

Recenzii

“This book describes in great detail and in a step‐wise fashion how to set up a network for an algorithm based decision-making system for treating tinnitus and assessing the effectiveness of treatment interventions for tinnitus. … This book is most suitable for medical practitioners who have been seeing tinnitus patients in their practice … . This book is geared towards clinicians who are also research-based and interested in taking an analytic and data-driven approach to the treatment of tinnitus.” (JoAnn Shih, Doody's Book Reviews, May, 2017)

Textul de pe ultima copertă

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.

Caracteristici

Describes a recommender system for tinnitus to help physicians diagnose and treat tinnitus patients Presents an introduction to the topic of tinnitus Highlights the basic concepts of recommender systems (RS), the current state-of-the-art and their real-world applications in different areas Focuses on health recommender systems Proposes a knowledge discovery approach to developing decision support systems Presents theoretical concepts and algorithms for rule-based systems, including: decision tables, classification rules, action rule extraction, and meta-actions Includes supplementary material: sn.pub/extras