Is the inter-patient coincidence of a subclinical disorder related to EHR similarity?

Electronic Health Record (EHR) provide clinical evidence for identifying subclinical diseases and supporting decisions on early intervention. Simple string matching cannot link up the conceptually similar but verbally different clinical terms in patient records, limiting the usefulness of EHR. A novel ontological similarity matching approach supported by the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) is proposed in this paper. The disease terms of a patient record are transformed into a vector space so that each patient record can be characterized by a feature vector. The similarity between the new record and an existing database record was quantified by a kernel function of their feature vectors. The matches are ranked by their similarity scores. To evaluate the proposed matching approach, medical history and carotid ultrasonic imaging finding were collected from 47 subjects in Hong Kong. The dataset formed 1081 pairs of patient records and the ROC analysis was used to evaluate and compare the accuracy of the ontological similarity matching and the simple string matching against the presence or absence of carotid plaques identified in ultrasound examination. It was found that the simple string matching randomly rated the record pairs but the ontological similarity matching provided non-random rating.