Paper Info
Title
Mining balance disorders' data for the development of diagnostic decision support systems.
Abstract
In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts.
Year
DOI
Venue
2016
10.1016/j.compbiomed.2016.08.016
Comp. in Bio. and Med.
Keywords
Field
DocType
Balance disorders,Data mining,Decision support systems,Vestibular system
Data science,Computer science,Decision support system,Expert system,Balance disorders,Global Positioning System,Physical examination
Journal
Volume
Issue
ISSN
77
C
0010-4825
Citations 
PageRank 
References 
1
0.43
8
Authors
17
Name
Order
Citations
PageRank
Themis P Exarchos123524.31
George Rigas27211.38
Athanasios Bibas354.01
Dimitrios Kikidis423.09
Christos Nikitas521.74
Floris L. Wuyts621.74
Berina Ihtijarevic721.40
Leen Maes821.74
Massimo Cenciarini9164.18
Christoph Maurer1021.40
Nora Macdonald1121.74
Doris Eva Bamiou1222.75
Linda Luxon1322.07
Marios Prasinos1432.76
George Spanoudakis151057108.40
D Koutsouris1615026.12
Dimitrios I. Fotiadis17941121.32