Title: Integrated optimisation method for personalised modelling and case studies for medical decision support

Authors: Nikola Kasabov, Yingjie Hu

Addresses: Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand. ' Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand

Abstract: Personalised modelling aims to create a unique computational diagnostic or prognostic model for an individual. The paper reports a new Integrated Method for Personalised Modelling (IMPM) that applies global optimisation of variables (features) and neighbourhood of appropriate data samples to create an accurate personalised model for an individual. The proposed IMPM allows for adaptation, monitoring and improvement of an individual|s model. Three medical decision support problems are used as illustrations: cancer diagnosis and profiling; risk of disease evaluation based on whole genome SNPs data; chronic disease decision support. The method leads to improved accuracy and unique personalised profiling that could be used for personalised treatment and personalised drug design.

Keywords: personalised modelling; optimisation; data analysis; medical decision support; decision support systems; medical DSS; prognostic modelling; diagnostic modelling; cancer diagnosis; cancer profiling; disease risk; risk assessment; single-nucleotide polymorphisms; SNPs data; chronic disease; personalised profiling; personalised treatment; personalised drug design; personalisation.

DOI: 10.1504/IJFIPM.2010.039123

International Journal of Functional Informatics and Personalised Medicine, 2010 Vol.3 No.3, pp.236 - 256

Received: 12 Jan 2011
Accepted: 24 Jan 2011
Published online: 17 Mar 2011 *

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