Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111210
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Type: Journal article
Title: Personalized medicine support system : resolving conflict in allocation to risk groups and predicting patient molecular response to targeted therapy
Author: Banjar, H.
Adelson, D.
Brown, F.
Leclercq, T.
Citation: Health Informatics - An International Journal, 2017; 6(2):1-21
Publisher: AIRCC Publishing Corporation
Issue Date: 2017
ISSN: 2319-3190
2319-2046
Statement of
Responsibility: 
Haneen Banjar, David Adelson, Fred Brown, and Tamara Leclercq
Abstract: Treatment management in cancer patients is largely based on the use of a standardized set of predictive and prognostic factors. The former are used to evaluate specific clinical interventions, and they can be useful for selecting treatments because they directly predict the response to a treatment. The latter are used to evaluate a patient’s overall outcomes, and can be used to identify the risks or recurrence of a disease. Current intelligent systems can be a solution for transferring advancements in molecular biology into practice, especially for predicting the molecular response to molecular targeted therapy and the prognosis of risk groups in cancer medicine. This framework primarily focuses on the importance of integrating domain knowledge in predictive and prognostic models for personalized treatment. Our personalized medicine support system provides the needed support in complex decisions and can be incorporated into a treatment guide for selecting molecular targeted therapies.
Keywords: Personalized medicine support system; molecular targeted therapy; predict molecular response; risk assessment; cancer treatment
Rights: Health Informatics - An International Journal, 2017; 6(2):1-21AIRCC Journal papers published under the Creative Commons Attribution (CC BY) license.
RMID: 0030073882
DOI: 10.5121/hiij.2017.6201
Published version: http://airccse.org/journal/hiij/index.html
Appears in Collections:Computer Science publications

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