临床医学系统术语SNOMED CT的特点及其应用

SNOMED CT已经成为国际上广泛关注的一种医学参考术语与信息编码。介绍了SNOMED CT的发展历史，着重介绍与分析了其核心内容与特点，最后介绍了SNOMED CT的应用情况。     

2018年版SNOMED CT国际药物模型体系简介

目的 对新版医学系统命名法-临床术语(Systematized Nomenclature of Medicine-Clinical Terms,SNOMED CT)中的国际药物模型进行介绍,为我国药物模型的构建提供参考.方法 对2018年7月更新的SNOMED CT国际药物模型的设计理念、药物分类体系进行介绍,并以含氨...     

SNOMED CT在临床路径中应用探讨

介绍医学系统命名法--临床术语(Systematized Nomenclature of Medicine-Clinical Terms,SNOMED CT)的历史发展、内容及应用情况,阐述临床路径的作用与实施原则,深入分析在临床路径中使用SNOMED CT的可能性、具体实施方法和重要意义.标准化术语的支持有利于推动临床路径的合理化调整与推广实施.     

2013版SNOMED CT顶层概念调整及属性关系

SNOMED CT是结构化的临床术语集。介绍2013年版的SNOMED CT新增顶层概念"SNOMED CT模型组件"及其亚类"连接概念"。概念含义的逻辑表示通过"定义属性"定义,所有可用作"关系类型"的概念都归在"连接概念"下:"|is a|关系"和"概念模型属性"中的59个"属性关系"。并详细说明"关系"如何完整定义概念,以期为构建中医临床术语系统的研究工作提供参考。     

SNOMED CT概念关系表达与语义检索

详细阐述SNOMED CT基于概念的组织框架、表示模式、关系模型、表达规则等,初步探索SNOMED CT在医疗数据表达、语义检索方面的应用,为研究制定我国临床诊疗术语标准提供参考借鉴,进一步推动临床医疗数据的处理、挖掘与分析研究。     

知识堂:医学信息学基本概念(2)

OpenEHR(Electronic Health Record) 是一套开放的EHR体系结构,其目标是实现EHR系统内部以及EHR系统之间的健康信息共享,由OpenEHR机构专门负责制定.OpenEHR规范主要包括参考模型(Reference Model,RM)、原型模型(Archetype Model,AM)和服务模型(Service Model,SM)3部分.     

基于本体的中西医知识体系对比研究

中医知识体系中包含大量的隐性知识,运用本体构建中医知识体系有利于中医隐性知识的表达与共享。通过对医学系统命名法-临床术语(SNOMED CT)和中医临床术语系统的研究,从本体论的角度分析中西医学知识特点,对发展中医进行提示:利用本体论构建中医知识体系,进而完善中医临床术语系统,促进中医临床知识共享。     

SNOMEDCT层次结构与标识符

SNOMED CT中的概念都通过层级关系相连.介绍了目前SNOMD CT的概念层级结构及分类情况,包括新增加的＂元数据层级＂、＂根元数据概念＂;还介绍了常用的几个标识符及SNOMED CT标识符（SCTID）的结构,包括数据类型、扩展、约束条件、验校码、分区标识符、命名空间等,为学习和借鉴SNOMD CT的标准化方法提供资料.     

《新编英汉医学信息学词汇》评价

《新编英汉医学信息学词汇》是由北京市卫生局信息中心组织多名国内医学信息学界专家编写而成。其主编为《国际医学规范术语全集》(SNOMED)汉译主编李恩生教授,SNOMED副主译解放军医学图书馆数据库研究部主任雷春炳教授以及SNOMED翻     

中医规范术语在结构化电子病历中应用体系的研究

中医临床规范术语如何为结构化电子病历（临床科研信息共享系统）提供支撑服务,不仅是周内亟待解决的问题,也是国际上面临的重要难题。研究以既往研究成果－借鉴SNOMED构建模型研制的《中医临床术语集》为基础,以中医结构化电子病历及数据挖掘平台为技术平台,通过建立术语字典这个中间体,将《中医临床术语集》中的术语集成映射到术语字典,实现了术语字典与病历模板编辑器的有机衔接,构建了中医临床规范术语在结构化电了病历中应用的办法及技术体系。     

Using SNOMED CT to Represent Two Interface Terminologies

Objective

Interface terminologies are designed to support interactions between humans and structured medical information. In particular, many interface terminologies have been developed for structured computer based documentation systems. Experts and policy-makers have recommended that interface terminologies be mapped to reference terminologies. The goal of the current study was to evaluate how well the reference terminology SNOMED CT could map to and represent two interface terminologies, MEDCIN and the Categorical Health Information Structured Lexicon (CHISL).

Design

Automated mappings between SNOMED CT and 500 terms from each of the two interface terminologies were evaluated by human reviewers, who also searched SNOMED CT to identify better mappings when this was judged to be necessary. Reviewers judged whether they believed the interface terms to be clinically appropriate, whether the terms were covered by SNOMED CT concepts and whether the terms' implied semantic structure could be represented by SNOMED CT.

Measurements

Outcomes included concept coverage by SNOMED CT for study terms and their implied semantics. Agreement statistics and compositionality measures were calculated.

Results

The SNOMED CT terminology contained concepts to represent 92.4% of MEDCIN and 95.9% of CHISL terms. Semantic structures implied by study terms were less well covered, with some complex compositional expressions requiring semantics not present in SNOMED CT. Among sampled terms, those from MEDCIN were more complex than those from CHISL, containing an average 3.8 versus 1.8 atomic concepts respectively, p<0.001.

Conclusion

Our findings support using SNOMED CT to provide standardized representations of information created using these two terminologies, but suggest that enriching SNOMED CT semantics would improve representation of the external terms.     

Research and applications: Semantic analysis of SNOMED CT for a post-coordinated database of histopathology findings

Objective

This research investigated the use of SNOMED CT to represent diagnostic tissue morphologies and notable tissue architectures typically found within a pathologist''s microscopic examination report to identify gaps in expressivity of SNOMED CT for use in anatomic pathology.

Methods

24 breast biopsy cases were reviewed by two board certified surgical pathologists who independently described the diagnostically important tissue architectures and diagnostic morphologies observed by microscopic examination. In addition, diagnostic comments and details were extracted from the original diagnostic pathology report. 95 unique clinical statements were extracted from 13 malignant and 11 benign breast needle biopsy cases.

Results

75% of the inventoried diagnostic terms and statements could be represented by valid SNOMED CT expressions. The expressions included one pre-coordinated expression and 73 post-coordinated expressions. No valid SNOMED CT expressions could be identified or developed to unambiguously assert the meaning of 21 statements (ie, 25% of inventoried clinical statements). Evaluation of the findings indicated that SNOMED CT lacked sufficient definitional expressions or the SNOMED CT concept model prohibited use of certain defined concepts needed to describe the numerous, diagnostically important tissue architectures and morphologic changes found within a surgical pathology microscopic examination.

Conclusions

Because information gathered during microscopic histopathology examination provides the basis of pathology diagnoses, additional concept definitions for tissue morphometries and modifications to the SNOMED CT concept model are needed and suggested to represent detailed histopathologic findings in computable fashion for purposes of patient information exchange and research.

Trial registration number

UNMC Institutional Review Board ID# 342-11-EP.     

Use of SNOMED CT to Represent Clinical Research Data: A Semantic Characterization of Data Items on Case Report Forms in Vasculitis Research

ObjectiveTo estimate the coverage provided by SNOMED CT for clinical research concepts represented by the items on case report forms (CRFs), as well as the semantic nature of those concepts relevant to post-coordination methods.DesignConvenience samples from CRFs developed by rheumatologists conducting several longitudinal, observational studies of vasculitis were selected. A total of 17 CRFs were used as the basis of analysis for this study, from which a total set of 616 (unique) items were identified. Each unique data item was classified as either a clinical finding or procedure. The items were coded by the presence and nature of SNOMED CT coverage and classified into semantic types by 2 coders.MeasurementsBasic frequency analysis was conducted to determine levels of coverage provided by SNOMED CT. Estimates of coverage by various semantic characterizations were estimated.ResultsMost of the core clinical concepts (88%) from these clinical research data items were covered by SNOMED CT; however, far fewer of the concepts were fully covered (that is, where all aspects of the CRF item could be represented completely without post-coordination; 23%). In addition, a large majority of the concepts (83%) required post-coordination, either to clarify context (e.g., time) or to better capture complex clinical concepts (e.g., disease-related findings). For just over one third of the sampled CRF data items, both types of post-coordination were necessary to fully represent the meaning of the item.ConclusionSNOMED CT appears well-suited for representing a variety of clinical concepts, yet is less suited for representing the full amount of information collected on CRFs.     

Using the wisdom of the crowds to find critical errors in biomedical ontologies: a study of SNOMED CT

Objectives The verification of biomedical ontologies is an arduous process that typically involves peer review by subject-matter experts. This work evaluated the ability of crowdsourcing methods to detect errors in SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and to address the challenges of scalable ontology verification.Methods We developed a methodology to crowdsource ontology verification that uses micro-tasking combined with a Bayesian classifier. We then conducted a prospective study in which both the crowd and domain experts verified a subset of SNOMED CT comprising 200 taxonomic relationships.Results The crowd identified errors as well as any single expert at about one-quarter of the cost. The inter-rater agreement (κ) between the crowd and the experts was 0.58; the inter-rater agreement between experts themselves was 0.59, suggesting that the crowd is nearly indistinguishable from any one expert. Furthermore, the crowd identified 39 previously undiscovered, critical errors in SNOMED CT (eg, ‘septic shock is a soft-tissue infection’).Discussion The results show that the crowd can indeed identify errors in SNOMED CT that experts also find, and the results suggest that our method will likely perform well on similar ontologies. The crowd may be particularly useful in situations where an expert is unavailable, budget is limited, or an ontology is too large for manual error checking. Finally, our results suggest that the online anonymous crowd could successfully complete other domain-specific tasks.Conclusions We have demonstrated that the crowd can address the challenges of scalable ontology verification, completing not only intuitive, common-sense tasks, but also expert-level, knowledge-intensive tasks.