Semantic Model of SNOMED as a Bayesian Network

作为贝叶斯网络的 SNOMED 语义模型

• 批准号: 6654454
• 负责人: KURT Lee ZIMMERMAN
• 金额: $ 6.12万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-03 至 2004-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6654454
• 关键词: clinical chemistry; computer network; data collection methodology /evaluation; drug /agent; information systems; laboratory rat; liver disorder; method development; pathology; postdoctoral investigator; toxicology; veterinary medicine

DESCRIPTION (provided by applicant): Clinical pathology data provides a basis for medical decision-making. Uncertainty is introduced into medical-decision-making by irreducible Biologic variability in patient data, ambiguities in communication of data, and the cognition of the decision-making process. Controlled medical terminologies (CMT) and medical decision support systems (DSS) reduce the uncertainty contributed by the latter two sources. Medical DSS are typically comprised of: an interface for data input and output, a knowledge base (KB) for storage of facts, and an inference engine for deriving new information. CMT employ knowledge representation formats similar to those used in DSS KBs. This study will evaluate the semantic network used by the Systematized Nomenclature of Medicine (SNOMED) for use as a KB in a Bayesian DSS to classify rodent liver disorders using clinical chemistry data. The primary aims of this proposal are to 1) assess SNOMED's ability to represent liver disorder concepts, 2) develop a formal methodology for adapting the semantic network of SNOMED for use in a Bayesian network and 3) evaluate the functionality of the resulting DSS using retrospective clinical chemistry data from drug toxicity studies. If SNOMED can functional as a KB to meet these aims, other domain-specific medical DSS can be created using the methodologies developed in this study.

描述（由申请人提供）： 临床病理学数据为医疗决策提供了依据。不确定性被引入到医疗决策中，这是由于患者数据中不可约的生物变异性、数据交流中的模糊性以及决策过程的认知。受控医学术语（CMT）和医疗决策支持系统（DSS）减少了后两个来源所带来的不确定性。医疗决策支持系统通常包括：用于数据输入和输出的接口，用于存储事实的知识库（KB），以及用于导出新信息的推理机。CMT采用类似于DSS知识库中使用的知识表示格式。本研究将评价系统化医学命名法（SNOMED）使用的语义网络，用作贝叶斯DSS中的KB，以使用临床化学数据对啮齿动物肝脏疾病进行分类。该提案的主要目的是：1）评估SNOMED表示肝脏疾病概念的能力，2）开发一种正式的方法，用于调整SNOMED的语义网络，以用于贝叶斯网络，3）使用药物毒性研究的回顾性临床化学数据评价所得DSS的功能。如果SNOMED可以作为一个知识库，以满足这些目标，其他特定领域的医疗决策支持系统可以创建使用本研究中开发的方法。

项目成果

SNOMED representation of explanatory knowledge in veterinary clinical pathology.

兽医临床病理学解释性知识的 SNOMED 表示。

• DOI: 10.1111/j.1939-165x.2005.tb00002.x
• 发表时间: 2005
• 期刊: Veterinary clinical pathology
• 影响因子: 1.2
• 作者: Zimmerman,KurtL;Wilcke,JeffR;Robertson,JohnL;Feldman,BernardF;Kaur,Taranjit;Rees,LorenR;Spackman,KentA
• 通讯作者: Spackman,KentA

Another disordered chameleon: the Micro-Exon Gene 14 protein from Schistosomiasis.

另一种紊乱的变色龙：来自血吸虫病的微外显子基因 14 蛋白。

• DOI: 10.1016/j.bpj.2013.04.018
• 发表时间: 2013
• 期刊: Biophysical journal
• 影响因子: 3.4
• 作者: Dunker,AKeith