OBJECTIVE: To use the notion of semantic distance to find the nearest neighbors of a medical concept in a controlled vocabulary. MATERIAL AND METHOD: 392 concepts from the cardiovascular chapter of the ICD-10 were projected on the axes of SNOMED III. Distances were measured on each axis and the resulting distance was found using a Lp norm. RESULTS: The distance between a set of ischemic diseases and a set of non-ischemic diseases was significant (p < 0.0001). Our method was validated by finding the k nearest neighbors of ten different diagnoses from the ICD-10 cardiovascular chapter. DISCUSSION: The availability of SNOMED-RT should improve our method. Several more steps are necessary to provide an ideal coding tool.
OBJECTIVE: To use the notion of semantic distance to find the nearest neighbors of a medical concept in a controlled vocabulary. MATERIAL AND METHOD: 392 concepts from the cardiovascular chapter of the ICD-10 were projected on the axes of SNOMED III. Distances were measured on each axis and the resulting distance was found using a Lp norm. RESULTS: The distance between a set of ischemic diseases and a set of non-ischemic diseases was significant (p < 0.0001). Our method was validated by finding the k nearest neighbors of ten different diagnoses from the ICD-10 cardiovascular chapter. DISCUSSION: The availability of SNOMED-RT should improve our method. Several more steps are necessary to provide an ideal coding tool.
Authors: Serguei V S Pakhomov; Ted Pedersen; Bridget McInnes; Genevieve B Melton; Alexander Ruggieri; Christopher G Chute Journal: J Biomed Inform Date: 2010-10-31 Impact factor: 6.317