title: Transcription of Case Report Forms from Unstructured Referral Letters: A Semantic Text Analytics Approach.
abstract: In this paper we present a framework for the semi-automatic extraction of medical entities from referral letters and use them to transcribe a case report form. Our framework offers the functionality to: (a) extract the medical entity from the unstructured referral letters, (b) classify them according to their semantic type, and (c) transcribe a case report form based on the extracted information from the referral letter. We take a semantic text analytics approach where SNOMED-CT ontology is used to both classify referral concepts and to establish semantic similarities between referral concepts and CRF elements. We used 100 spine injury referral letters, and a standard case report form used by Association of Dalhousie Neurosurgeons, Dalhousie University.
authors: NICHE Research Lab, Faculty of Computer Science, Dalhousie University, Halifax, Canada.
authors: Syed Sibte Raza
authors: SS
authors: Abidi
authors: NICHE Research Lab, Faculty of Computer Science, Dalhousie University, Halifax, Canada.
authors: Abhinav Kumar
authors: AK
authors: Singh
authors: Division of Neurosurgery, Dalhousie University, Halifax, Canada.
authors: Sean
authors: S
authors: Christie
mesh_terms: D015709:Correspondence as Topic; D001330:Electronic Data Processing; D016247:Information Storage and Retrieval; D012017:Referral and Consultation; D012660:Semantics; D039061:Systematized Nomenclature of Medicine