Decision support system for diagnosis and treatment of hearing disorders : the case of tinnitus

To chci

"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."--Nakladatelská anotace … celý popis

Podrobná bibliografie

Hlavní autor

Katarzyna A. Tarnowska

Další autoři

Zbigniew Wiesław Raś, 1947-
Pawel J. Jastreboff
SpringerLink (online služba)

Typ dokumentu

Knihy

Fyzický popis

1 online zdroj (x, 160 stran) : ilustrace

Vydáno

Cham, Switzerland : Springer, [2017]

Edice

Studies in computational intelligence

Témata

poruchy sluchu > zd

ušní šelesty

diagnóza > zd

diagnostika (lékařství)

zpracování dat > vt

analýza dat

informatika > if

zdravotnická informatika

umělá inteligence > vt

computational intelligence

výpočetní inteligence

kolektivní monografie

elektronické knihy

ISBN

978-3-319-51463-5

Obsah

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