Τίτλος:
Mining balance disorders' data for the development of diagnostic decision support systems
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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. © 2016 Elsevier Ltd
Συγγραφείς:
Exarchos, T.P.
Rigas, G.
Bibas, A.
Kikidis, D.
Nikitas, C.
Wuyts, F.L.
Ihtijarevic, B.
Maes, L.
Cenciarini, M.
Maurer, C.
Macdonald, N.
Bamiou, D.-E.
Luxon, L.
Prasinos, M.
Spanoudakis, G.
Koutsouris, D.D.
Fotiadis, D.I.
Περιοδικό:
Computers in Biology and Medicine
Εκδότης:
Elsevier Ireland Ltd
Λέξεις-κλειδιά:
Artificial intelligence; Data mining; Diagnosis; Medical computing, Balance disorders; Clinical examination; Decision supports; Demographic characteristics; Diagnostic decisions; Diagnostic process; General practitioners; Vestibular system, Decision support systems, acoustic neuroma; Article; balance disorder; benign paroxysmal positional vertigo; bilateral peripheral dysfunction; clinical examination; data mining; decision support system; decision tree; dizziness; hearing test; human; major clinical study; Meniere disease; migrainous vertigo; predictive value; priority journal; sensitivity and specificity; unilateral peripheral dysfunction; vertigo; vestibular neuronitis; vestibular paroxysmia; vestibular test; algorithm; body equilibrium; clinical decision support system; data mining; physiology; procedures; vertigo; vestibular labyrinth, Algorithms; Data Mining; Decision Support Systems, Clinical; Decision Support Techniques; Decision Trees; Humans; Postural Balance; Vertigo; Vestibule, Labyrinth
DOI:
10.1016/j.compbiomed.2016.08.016
