Knowledge Integration for Disease Characterization: A Breast Cancer Example

Merrill Hall October 11, 2018 12:00 - 12:20

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Oshani Seneviratne, Sabbir Rashid, Shruthi Chari, Jim McCusker, Kristin Bennett, James Hendler and Deborah McGuinness.  

Abstract:  With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when practicing oncologists and physicians try to remain current. One example of this involves increasing usage of biomarkers when characterizing the pathologic prognostic stage of a breast tumor. We present our semantic technology approach to support cancer characterization and demonstrate it in our end-to-end prototype system that collects the newest breast cancer staging criteria from authoritative oncology manuals to construct an ontology for breast cancer. Using a tool we developed that utilizes this ontology, physicians can quickly stage a new patient to support identifying risks, treatment options, and monitoring plans based on authoritative and best practice guidelines. Physicians can also re-stage an existing patient, allowing them to find patients whose stage has changed in a given patient cohort. As new guidelines emerge, using our proposed mechanism, which is grounded by semantic technologies for ingesting new data from staging manuals, we have created an enriched cancer staging ontology that integrates relevant data from several sources with very little human intervention.

Keywords:  ontologies;  knowledge integration;  deductive inferencer;  automatic extraction;  cancer characterization;  cancer staging guidelines