Title: Mining of patient data: towards better treatment strategies for depression

Authors: Maja Hadzic, Fedja Hadzic, Tharam S. Dillon

Addresses: Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, G.P.O. Box U1987, Perth, WA 6845, Australia. ' Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, G.P.O. Box U1987, Perth, WA 6845, Australia. ' Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, G.P.O. Box U1987, Perth, WA 6845, Australia

Abstract: An intelligent system based on data-mining technologies that can be used to assist in the prevention and treatment of depression is described. The system integrates three different kinds of patient data as well as the data describing mental health of therapists and their interaction with the patients. The system allows for the different data to be analysed in a conjoint manner using both traditional data-mining techniques and tree-mining techniques. Interesting patterns can emerge in this way to explain various processes and dynamics involved in the onset, treatment and management of depression, and help practitioners develop better prevention and treatment strategies.

Keywords: depression treatment; depression prevention; data mining; XML mining; data analysis; personalised care; personalised treatment; patient data; mental health; therapists; tree mining.

DOI: 10.1504/IJFIPM.2010.037150

International Journal of Functional Informatics and Personalised Medicine, 2010 Vol.3 No.2, pp.122 - 143

Received: 06 Jun 2010
Accepted: 15 Sep 2010

Published online: 29 Nov 2010 *

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