Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2000 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | Semantic similarity,k-nearest neighbors algorithm,Computer science,Lp space,Controlled vocabulary,Coding (social sciences),Theoretical computer science,SNOMED CT,Semantics,Medical diagnosis |
DocType | Issue | ISSN |
Conference | SUPnan | 1067-5027 |
Citations | PageRank | References |
4 | 0.49 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cédric Bousquet | 1 | 109 | 22.59 |
| Marie-Christine Jaulent | 2 | 375 | 68.72 |
| G Chatellier | 3 | 59 | 11.92 |
| Patrice Degoulet | 4 | 403 | 52.74 |