Automated Diagnosis Through Ontologies and Logical Descriptions: The ADONIS Approach
International Journal of Decision Support System Technology
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The most uncertain and difficult medical decision in Colon Cancer (CC) is the selection of the most personalized treatment option [1, 4, 5, 7, 12, 13]. MATCH Automated Diagnosis System has been primarily developed to target those obstacles, assisting clinicians in the post-diagnosis phases of the investigations. MATCH system is being currently fed with hundreds of data samples that range from clinical information (including follow-up from every treatment applied), tumor markers [1], proteomic sequences, gene suppressors and SNPs functional correlations. Each dimension of the data is described in MATCH CC ontology. The importance of all data attributes and the corresponding processing information is modifiable, but the default statistical model has been developed together with oncology experts from Jagiellonian Medial College and FONDAZIONE IOM. Health professionals using MATCH system will be able to query the database of anonymised clinical and molecular information. In this paper we will present a design of the MATCH system with the special emphasis on the semantic modeling as the authors' contribution to the project. The MATCH CC ontology, created as a part of knowledge management layer, is a basis of MATCH logical architecture and processing [2, 3]. The MATCH system is developed with Web-Services, which assure the interoperability between hospitals, pharmaceutical laboratories and research centers, which have access and possibility to exchange samples, mined information and data models