DocumentCode
2335479
Title
Discovering similar patterns for characterising time series in a medical domain
Author
Alonso, Fernando ; Caraça-Valente, Juan P. ; Martínez, Loïc ; Montes, Cesar
Author_Institution
Dept. Languages & Syst., Polytech. Univ. Madrid, Spain
fYear
2001
fDate
2001
Firstpage
577
Lastpage
579
Abstract
In this article, we describe the process of discovering similar patterns in time series and creating reference models for population groups in a medical domain, and particularly in the field of physiotherapy, using data mining techniques on a set of isokinetic data. The discovered knowledge was evaluated against the expertise of a physician specialising in isokinetic techniques, and applied in the I4 (Intelligent Interpretation of Isokinetic Information) project developed in conjunction with the Spanish National Centre for Sports Research and Sciences and the School of Physiotherapy of the Spanish National Organisation for the Blind for muscular diagnosis and rehabilitation, injury prevention, training evaluation and planning, etc., of elite and blind athletes
Keywords
data mining; medical computing; muscle; patient diagnosis; patient rehabilitation; patient treatment; pattern classification; sport; time series; training; I4 project; blind athletes; data mining techniques; elite athletes; injury prevention; intelligent information interpretation; isokinetic data set; medical domain; muscular diagnosis; muscular rehabilitation; physician expertise; physiotherapy; population groups; reference models; similar patterns discovery; sports research; time series characterisation; training evaluation; training planning; Algorithm design and analysis; Artificial intelligence; Data mining; Delta modulation; Injuries; Instruments; Knee; Medical diagnostic imaging; Muscles; Physics computing;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location
San Jose, CA
Print_ISBN
0-7695-1119-8
Type
conf
DOI
10.1109/ICDM.2001.989567
Filename
989567
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