"Understanding" medical school curriculum content using KnowledgeMap
"Understanding" medical school curriculum content using KnowledgeMap
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DOI：
10.1197/jamia.m1176
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发表时间：
2003-07-01
影响因子：
6.4
通讯作者：
Spickard, A
中科院分区：
文献类型：
--
作者：
Denny, JC;Smithers, JD;Spickard, A
目的：描述计算工具的开发和评估，以确定医学课程文件中的概念，使用来自国家医学图书馆的统一医学语言系统（UMLS）的信息。KnowledgeMap（KM）项目的长期目标是为教师和学生提供更好的能力，开发，审查和整合组件的医学院courses.Design：KM概念标识符使用的词汇资源部分来自UMLS（专业词典和元词库），启发式语言处理技术，和经验评分算法。KM区分源文档中可能匹配的元词库概念。作者在选定的医学院完整内容讲座文档中手动识别重要的“黄金标准”生物医学概念，并使用这些文档将KM概念识别与已知的最先进的“标准”-国家医学图书馆的MetaMap程序进行比较。每个讲座文档中由KM或MetaMap识别的“黄金标准”概念的数量，结果：对于4，281个“金标准”概念，MetaMap的匹配率为78%，KM为82%。MetaMap和KM的“黄金标准”概念的精确度分别为85%和89%。知识管理的词汇准确地匹配了首字母缩略词、文档中未详细说明的概念和模糊的匹配。匹配失败的最常见的原因是没有目标的概念从UMLS Metathesaurus.Conclusion：原型KM系统提供了一个令人鼓舞的代表医学课程文本的概念提取率。未来版本的知识管理应评估他们的能力，让管理员，讲师和学生浏览医学课程，以找到冗余，找到相关的信息，并确定遗漏。此外，应评估知识管理满足具体的个人信息需求的能力。
Objective: To describe the development and evaluation of computational tools to identify concepts within medical curricular documents, using information derived from the National Library of Medicine's Unified Medical Language System (UMLS). The long-term goal of the KnowledgeMap (KM) project is to provide faculty and students with an improved ability to develop, review, and integrate components of the medical school curriculum.Design: The KM concept identifier uses lexical resources partially derived from the UMLS (SPECIALIST lexicon and Metathesaurus), heuristic language processing techniques, and an empirical scoring algorithm. KM differentiates among potentially matching Metathesaurus concepts within a source document. The authors manually identified important "gold standard" biomedical concepts within selected medical school full-content lecture documents and used these documents to compare KM concept recognition with that of a known state-of-the-art "standard"-the National Library of Medicine's MetaMap program.Measurements: The number of "gold standard" concepts in each lecture document identified by either KM or MetaMap, and the cause of each failure or relative success in a random subset of documents.Results: For 4,281 "gold standard" concepts, MetaMap matched 78% and KM 82%. Precision for "gold standard" concepts was 85% for MetaMap and 89% for KM. The heuristics of KM accurately matched acronyms, concepts underspecified in the document, and ambiguous matches. The most frequent cause of matching failures was absence of target concepts from the UMLS Metathesaurus.Conclusion: The prototypic KM system provided an encouraging rate of concept extraction for representative medical curricular texts. Future versions of KM should be evaluated for their ability to allow administrators, lecturers, and students to navigate through the medical curriculum to locate redundancies, find interrelated information, and identify omissions. In addition, the ability of KM to meet specific, personal information needs should be assessed.