Interpreting Patient Data using Medical Background Knowledge

Clinical patient data, such as medical images and reports, establish the basis of the diagnostic process. In order to improve the access to heterogeneous and distributed clinical data sources recent work concentrates on extracting semantic annotations with links to concepts of medical ontologies, such as RadLex, FMA, SNOMED CT or others. However, these annotations are not always on the appropriate level of detail for clinical applications as they simply reflect the descriptive content of the respective patient data. For integrating the data into the clinical work-flow, an interpretation of annotations using medical background knowledge is needed. In this paper we present a use-case of such an interpretation: we have built an initial ontology containing lymphoma-related diseases and symptoms as well as their relations. The created ontology is used to infer likely diseases of patients based on annotations. In this way, annotations can be understood in the context of likely diseases and help the clinician to make a diagnosis. By means of a prototype implementation we evaluate our approach and identify further knowledge requirements for the model.