Névéol Aurélie, Soualmia Lina F, Douyère Magaly, Rogozan Alexandrina, Thirion Benoît, Darmoni Stefan J
CISMeF, Rouen University Hospital, Rouen Cedex, France.
Int J Med Inform. 2004 Feb;73(1):57-64. doi: 10.1016/j.ijmedinf.2003.09.004.
CISMeF is a Quality Controlled Health Gateway using a terminology based on the Medical Subject Headings (MeSH) thesaurus that displays medical specialties (metaterms) and the relationships existing between them and MeSH terms.
The need to classify the resources within the catalogue has led us to combine this type of semantic information with domain expert knowledge for health resources categorization purposes.
A two-step categorization process consisting of mapping resource keywords to CISMeF metaterms and ranking metaterms by decreasing coverage in the resource has been developed. We evaluate this algorithm on a random set of 123 resources extracted from the CISMeF catalogue. Our gold standard for this evaluation is the manual classification provided by a domain expert, viz. a librarian of the team.
The CISMeF algorithm shows 81% precision and 93% recall, and 62% of the resources were assigned a "fully relevant" or "fairly relevant" categorization according to strict standards.
A thorough analysis of the results has enabled us to find gaps in the knowledge modeling of the CISMeF terminology. The necessary adjustments having been made, the algorithm is currently used in CISMeF for resource categorization.
CISMeF是一个质量可控的健康网关,它使用基于医学主题词表(MeSH)的术语,展示医学专科(元术语)以及它们与MeSH术语之间存在的关系。
对目录中的资源进行分类的需求促使我们为了健康资源分类目的,将这种语义信息与领域专家知识相结合。
已开发出一个两步分类过程,包括将资源关键词映射到CISMeF元术语,并按资源中覆盖范围递减对元术语进行排序。我们在从CISMeF目录中提取的123个资源的随机集合上评估此算法。本次评估的黄金标准是由领域专家(即团队中的一名图书管理员)提供的人工分类。
CISMeF算法显示出81%的精确率和93%的召回率,并且根据严格标准,62%的资源被归类为“完全相关”或“相当相关”。
对结果的深入分析使我们能够发现CISMeF术语知识建模中的差距。在进行了必要的调整后,该算法目前在CISMeF中用于资源分类。