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.     

SNOMED CT与OpenEHR的整合探究

详细介绍SNOMED CT与OpenEHR的发展历史、主要内容、主要架构,通过数据类型转换、原型中的临床术语与SNOMED CT的映射、候选术语集等方法进行SNOMED CT与OpenEHR的整合,从而使原型中的术语更加规范,也为互操作提供可能。     

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。     

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.     

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。     

系统化医学术语-临床术语集疾病分类及概念模型研究

介绍系统化医学术语-临床术语集(Systematized Nomenclature of Medicine Clinical Terms, SNOMED CT)概念及应用,阐述SNOMED CT疾病分类与疾病概念模型、6类疾病定义模式以及对我国中医疾病概念模型开发的启示,为我国中医疾病术语集构建提供参考。     

正电子发射断层显像/X线计算机体层成像检查的临床应用与编码

目的了解正电子发射断层显像/X线计算机体层成像（PET/CT）检查的应用与编码。方法 PET/CT是PET的功能显像与螺旋CT的精细结构显像两种最高档显像技术的优点融于一体,通过病案分析了解它的先进性及编码相应的章节。结果 PET/CT可为医师提供准确前瞻性指导性建议,编码分类在＂核医学（92）＂。结论根据PET/CT检查结果,可改变对患者的治疗方案,确定病情进展阶段。编码员要仔细阅读病历,做好疾病、手术及医疗操作检查的编码工作。     

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.     

Auditing the Semantic Completeness of SNOMED CT Using Formal Concept Analysis

Objective

This study sought to develop and evaluate an approach for auditing the semantic completeness of the SNOMED CT contents using a formal concept analysis (FCA)-based model.

Design

We developed a model for formalizing the normal forms of SNOMED CT expressions using FCA. Anonymous nodes, identified through the analyses, were retrieved from the model for evaluation. Two quasi-Poisson regression models were developed to test whether anonymous nodes can evaluate the semantic completeness of SNOMED CT contents (Model 1), and for testing whether such completeness differs between 2 clinical domains (Model 2). The data were randomly sampled from all the contexts that could be formed in the 2 largest domains: Procedure and Clinical Finding. Case studies (n = 4) were performed on randomly selected anonymous node samples for validation.

Measurements

In Model 1, the outcome variable is the number of fully defined concepts within a context, while the explanatory variables are the number of lattice nodes and the number of anonymous nodes. In Model 2, the outcome variable is the number of anonymous nodes and the explanatory variables are the number of lattice nodes and a binary category for domain (Procedure/Clinical Finding).

Results

A total of 5,450 contexts from the 2 domains were collected for analyses. Our findings revealed that the number of anonymous nodes had a significant negative correlation with the number of fully defined concepts within a context (p < 0.001). Further, the Clinical Finding domain had fewer anonymous nodes than the Procedure domain (p < 0.001). Case studies demonstrated that the anonymous nodes are an effective index for auditing SNOMED CT.

Conclusion

The anonymous nodes retrieved from FCA-based analyses are a candidate proxy for the semantic completeness of the SNOMED CT contents. Our novel FCA-based approach can be useful for auditing the semantic completeness of SNOMED CT contents, or any large ontology, within or across domains.     

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。     

多层螺旋CT概念解析

多层螺旋CT（multi—slice spiral CT）的研发与普及,进一步拓宽了CT影像技术的临床应用范围,将CT诊断水平推向了一个崭新的阶段,是CT发展史上一个重要的里程碑。多层螺旋CT在设计上采用了多排探测器结构,显著提高了螺旋CT的各项性能指标,与单层螺旋CT相比,其扫描时间更短、扫描层厚更薄、扫描范围更大、重建质量更好、管球效能更高。学习和应用多层螺旋CT时会涉及到一些新的概念,其中有些是在原有非螺旋或单层螺旋CT概念的基础上又赋予了新的内涵。如何科学、缜密地理解这些概念,     

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...     

基于语义相似度计算的临床诊断自动编码算法研究

提出一种为中文临床诊断自动进行ICD-10编码的算法,利用分布式语义相似度计算方法计算文本语义相似度,考虑到中文的语言特点,不仅基于词语构建词向量,还基于汉字构建词向量,测试二者对查准率和查全率的影响.结果显示该算法在测试集上获得较高的准确率.     

2007年ACCF/AHA CT冠状动脉钙化评分专家共识

冠状动脉钙化(CAC)是动脉粥样硬化的一部分。在粥样硬化动脉中均存在钙化,而在正常动脉管壁则很少见钙化。因而,及时检测CAC可早期发现冠状动脉粥样硬化。最近美国心脏病学会基金会(ACCF)和美国心脏学会(AHA)发布了专家共识,介绍了通过CT检测CAC积分在评估整体心血管危险和评价     

An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT

Objective Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the emergent international health terminology standard for encoding clinical information in electronic health records. The CORE Problem List Subset was created to facilitate the terminology’s implementation. This study evaluates the CORE Subset’s coverage and examines its growth pattern as source datasets are being incorporated.Methods Coverage of frequently used terms and the corresponding usage of the covered terms were assessed by “leave-one-out” analysis of the eight datasets constituting the current CORE Subset. The growth pattern was studied using a retrospective experiment, growing the Subset one dataset at a time and examining the relationship between the size of the starting subset and the coverage of frequently used terms in the incoming dataset. Linear regression was used to model that relationship.Results On average, the CORE Subset covered 80.3% of the frequently used terms of the left-out dataset, and the covered terms accounted for 83.7% of term usage. There was a significant positive correlation between the CORE Subset’s size and the coverage of the frequently used terms in an incoming dataset. This implies that the CORE Subset will grow at a progressively slower pace as it gets bigger.Conclusion The CORE Problem List Subset is a useful resource for the implementation of Systematized Nomenclature of Medicine Clinical Terms in electronic health records. It offers good coverage of frequently used terms, which account for a high proportion of term usage. If future datasets are incorporated into the CORE Subset, it is likely that its size will remain small and manageable.     

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.     

Getting the foot out of the pelvis: modeling problems affecting use of SNOMED CT hierarchies in practical applications

Objectives

(a) To determine the extent and range of errors and issues in the Systematised Nomenclature of Medicine – Clinical Terms (SNOMED CT) hierarchies as they affect two practical projects. (b) To determine the origin of issues raised and propose methods to address them.

Methods

The hierarchies for concepts in the Core Problem List Subset published by the Unified Medical Language System were examined for their appropriateness in two applications. Anomalies were traced to their source to determine whether they were simple local errors, systematic inferences propagated by SNOMED''s classification process, or the result of problems with SNOMED''s schemas. Conclusions were confirmed by showing that altering the root cause and reclassifying had the intended effects, and not others.

Main results

Major problems were encountered, involving concepts central to medicine including myocardial infarction, diabetes, and hypertension. Most of the issues raised were systematic. Some exposed fundamental errors in SNOMED''s schemas, particularly with regards to anatomy. In many cases, the root cause could only be identified and corrected with the aid of a classifier.

Limitations

This is a preliminary ‘experiment of opportunity.’ The results are not exhaustive; nor is consensus on all points definitive.

Conclusions

The SNOMED CT hierarchies cannot be relied upon in their present state in our applications. However, systematic quality assurance and correction are possible and practical but require sound techniques analogous to software engineering and combined lexical and semantic techniques. Until this is done, anyone using SNOMED codes should exercise caution. Errors in the hierarchies, or attempts to compensate for them, are likely to compromise interoperability and meaningful use.