Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis: An Integrated Approach

被引：0

作者：

Verma, Anju ^{[1
]}

Fiasche, Maurizio ^{[2
,3
]}

Cuzzola, Maria

Iacopino, Pasquale ^{[3
]}

Morabito, Francesco C. ^{[2
]}

Kasabov, Nikola ^{[1
]}

机构：

[1] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland, New Zealand

[2] Univ Mediterranea Reggio Calabria, DIMET, Calabria, Italy

[3] Transplant Regional Ctr Stem Cells & Cell, Reggio Di Calabria, Italy

来源：

NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS | 2009年 / 5864卷

关键词：

disease ontology; integrated approach; personalized modeling; diabetes; ARCHIMEDES; VALIDATION; SCORE; TOOL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel ontology based type 2 diabetes risk analysis system framework is described, which allows the creation of global knowledge representation (ontology) and personalized modeling for a decision support system. A computerized model focusing on organizing knowledge related to three chronic diseases and genes has been developed in an ontological representation that is able to identify interrelationships for the ontology-based personalized risk evaluation for chronic diseases. The personalized modeling is a process of model creation for a single person, based on their personal data and the information available in the ontology. A transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk for chronic disease. This approach aims to provide support for further discovery through the integration of the ontological representation to build an expert system in order to pinpoint genes of interest and relevant diet components.

引用

页码：360 / +

页数：3

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