A semantic feature space for disease prediction

The huge amount of data generated by modern medicine has motivated us to develop decision support systems for improving health care applications. In this paper, we address the problem of clinical disease prediction given patient-reported symptoms and medical signs where patient records lack of semantic code annotation. We propose a novel context-enhanced disease prediction approach based on leveraging semantic and contextual medical entity relations. We have already exploited semantic relations of medical terminology for patient records search [2] but they were never considered for disease prediction in the literature. Patient signs and symptoms are first mapped to SNOMED-CT concepts, which compose a feature space for disease prediction. Our major contributions in this paper consist of expanding the feature space using semantic and contextual concept relations of SNOMED-CT. Based on patient´s reported signs and symptoms, we use biomedical text mining tool, namely Metamap [1] to extract concepts of the SNOMED-CT metathesaurus. A “concept” in SNOMED-CT is a clinical meaning identified by a unique numeric identifier (ConceptId) and described via a set of words. For each concept, we define a medical entity context by integrating “defining” and “qualitative” medical aspects through the use of different types of semantic and contextual relationships of SNOMED-CT. Figure 1 illustrates the concept “Pneumonia” and its relations to other concepts.