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Mining interesting clinico‐genomic associations: The healthobs approach

Moustakis Vasilis, George Potamias, Lefteris Koumakis, Alexandros Kanterakis, Dimitrsi Kafetzopoulos, Manolis Tsiknakis

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URI: http://purl.tuc.gr/dl/dias/A7BD4AE5-37F0-4F85-85D8-6C398D8FA3B0
Year 2007
Type of Item Conference Full Paper
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Bibliographic Citation G. Potamias, L. Koumakis, A. Kanterakis, V. Moustakis, D. Kafetzopoulos, and M. Tsiknakis, "Mining Interesting Clinico‐Genomic Associations: The HealthObs Approach", in 4th IFIP Conference on Artificial Intelligence Applications and Innovations, 2007, pp. 137‐145, doi: 10.1007/978-0-387-74161-1_15 https://doi.org/10.1007/978-0-387-74161-1_15
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Conference Publications in Community School of Production Engineering and Management

Summary

HealthObs is an integrated (Java-based) environment targeting the seamless integration and intelligent processing of distributed and heterogeneous clinical and genomic data. Via the appropriate customization of standard medical and genomic data-models HealthObs achieves the semantic homogenization of remote clinical and gene-expression records, and their uniform XML-based representation. The system utilizes data-mining techniques (association rules mining) that operate on top of query-specific XML documents. Application of HealthObs on a real world breast-cancer clinico-genomic study demonstrates the utility and efficiency of the approach.

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