效度概念新认识

本文主要讨论了效度概念的演变和存在的问题、效度概念新认识及如何寻找效度三个问题。效度概念的发展从内容效度、预测效度、构想效度和同时效度并存发展到现在由构想效度涵盖了其它所有效度。但一直以来,把效度概念作为一个法理学网络来理解,造成了我们对效度概念理解的很多误区。事实上效度概念是一种本体论的观点,重视现实参照并依据因果关系。我们应该通过建立一个理论假设、形成一个测验和确定测量的属性来寻找效度。     

用本体论方法构建国家卫生信息标准基础框架

目的构建国家卫生信息标准基础框架.方法将本体论的方法应用于卫生信息领域,自顶向下构建国家卫生信息标准基础框架.结果①明确了国家卫生信息的范围：4个主域、11个亚域和53构件.②确立了国家卫生信息的数据模型及其层级关系：国家卫生信息数据模型的层级关系从大到小依次为信息框架、承接关系数据模型、概念数据模型、逻辑数据模型;③归纳出数据中的实体类,明确了类间的层级关系：所有的数据按照逻辑关系划分为11个超级实体（super-entities）,即业务因素、事件、费用、支撑条件、位置、参与者角色、参与者特征、结局、卫生服务计划、卫生服务需求、环境因素.根据超级实体表达信息的复杂程度,进一步将其细化为子实体,子子实体等.④确定实体类的属性及与其相关的数据元.结论本体论的方法可以作为构建国家卫生信息标准基础框架的基本技术路线.     

本体心理学在精神分裂症临床诊断与治疗中的应用

精神分裂症至今仍被认为是世界范围内广泛存在的最神秘的精神疾病.本体心理学流派经过对来自不同文化背景的患者40年成功的临床实践,第一个也是唯一一个证明了它能够运用有效方法 来治愈精神分裂症.文章描述了心身疾病和精神分裂症的病因,并从本体心理学的三个发现,即:偏差屏、语义场和本体自在分析了疾病生成过程,从而澄清和解决了许多有分歧的观点,并围绕着疾病的一些悬而未决的问题,以本体心理学的方法 进行了深入、详细的阐述.     

中医文本信息抽取系统

近年来,文本信息抽取成为中医文献知识挖掘的一种新兴手段。构建了基于本体的中医文本信息抽取系统,它能从中医文献中提取领域概念及语义关系,并支持用户完成文本语义关系的检阅、分析和标注等工作。该系统能辅助中医专家从中医文献中挖掘知识,并进一步完善中医领域本体系统。     

基于语义网和SOA的医学图书馆信息服务模型设计

提出了基于语义网关联数据和本体技术的用户检索方式的转变及基于本体的学科馆员信息服务模型。该模型利用SOA技术为基于语义网的信息服务提供了开放的应用接口,通过建立基于关联数据技术的驱动引擎和知识库,使资源之间具有了语义上的可扩展的关联关系,并搭建了学科馆员平台、医学领域专家平台、学科专业用户平台,学科馆员在医学领域专家的协助下,应用本体模型为医学专业用户提供语义层面的智能化检索服务。     

Early prediction of encephalopathic transformation in children with benign epilepsy with centro-temporal spikes

BackgroundMost children with Benign epilepsy with centro-temporal spikes (BECTS) undergo remission during late adolescence and do not require treatment. In a small group of patients, the condition may evolve to encephalopathic syndromes including epileptic encephalopathy with continuous spike-and-wave during sleep (ECSWS), or Landau-Kleffner Syndrome (LKS). Development of prediction models for early identification of at-risk children is of utmost importance.AimTo develop a predictive model of encephalopathic transformation using data-driven approaches, reveal complex interactions to identify potential risk factors.MethodsData were collected from a cohort of 91 patients diagnosed with BECTS treated between the years 2005–2017 at a pediatric neurology institute. Data on the initial presentation was collected based on a novel BECTS ontology and used to discover potential risk factors and to build a predictive model. Statistical and machine learning methods were compared.ResultsA subgroup of 18 children had encephalopathic transformation. The least absolute shrinkage and selection operator (LASSO) regression Model with Elastic Net was able to successfully detect children with ECSWS or LKS. Sensitivity and specificity were 0.83 and 0.44. The most notable risk factors were fronto-temporal and temporo-parietal localization of epileptic foci, semiology of seizure involving dysarthria or somatosensory auras.ConclusionNovel prediction model for early identification of patients with BECTS at risk for ECSWS or LKS. This model can be used as a screening tool and assist physicians to consider special management for children predicted at high-risk. Clinical application of machine learning methods opens new frontiers of personalized patient care and treatment.     

Validating an ontology-based algorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records

BackgroundImproving healthcare for people with chronic conditions requires clinical information systems that support integrated care and information exchange, emphasizing a semantic approach to support multiple and disparate Electronic Health Records (EHRs). Using a literature review, the Australian National Guidelines for Type 2 Diabetes Mellitus (T2DM), SNOMED-CT-AU and input from health professionals, we developed a Diabetes Mellitus Ontology (DMO) to diagnose and manage patients with diabetes. This paper describes the manual validation of the DMO-based approach using real world EHR data from a general practice (n = 908 active patients) participating in the electronic Practice Based Research Network (ePBRN).MethodThe DMO-based algorithm to query, using Semantic Protocol and RDF Query Language (SPARQL), the structured fields in the ePBRN data repository were iteratively tested and refined. The accuracy of the final DMO-based algorithm was validated with a manual audit of the general practice EHR. Contingency tables were prepared and Sensitivity and Specificity (accuracy) of the algorithm to diagnose T2DM measured, using the T2DM cases found by manual EHR audit as the gold standard. Accuracy was determined with three attributes – reason for visit (RFV), medication (Rx) and pathology (path) – singly and in combination.ResultsThe Sensitivity and Specificity of the algorithm were 100% and 99.88% with RFV; 96.55% and 98.97% with Rx; and 15.6% and 98.92% with Path. This suggests that Rx and Path data were not as complete or correct as the RFV for this general practice, which kept its RFV information complete and current for diabetes. However, the completeness is good enough for this purpose as confirmed by the very small relative deterioration of the accuracy (Sensitivity and Specificity of 97.67% and 99.18%) when calculated for the combination of RFV, Rx and Path. The manual EHR audit suggested that the accuracy of the algorithm was influenced by data quality such as incorrect data due to mistaken units of measurement and unavailable data due to non-documentation or documented in the wrong place or progress notes, problems with data extraction, encryption and data management errors.ConclusionThis DMO-based algorithm is sufficiently accurate to support a semantic approach, using the RFV, Rx and Path to define patients with T2DM from EHR data. However, the accuracy can be compromised by incomplete or incorrect data. The extent of compromise requires further study, using ontology-based and other approaches.     

Reuse of termino-ontological resources and text corpora for building a multilingual domain ontology: An application to Alzheimer’s disease

Ontologies are useful tools for sharing and exchanging knowledge. However ontology construction is complex and often time consuming. In this paper, we present a method for building a bilingual domain ontology from textual and termino-ontological resources intended for semantic annotation and information retrieval of textual documents. This method combines two approaches: ontology learning from texts and the reuse of existing terminological resources. It consists of four steps: (i) term extraction from domain specific corpora (in French and English) using textual analysis tools, (ii) clustering of terms into concepts organized according to the UMLS Metathesaurus, (iii) ontology enrichment through the alignment of French and English terms using parallel corpora and the integration of new concepts, (iv) refinement and validation of results by domain experts. These validated results are formalized into a domain ontology dedicated to Alzheimer’s disease and related syndromes which is available online (http://lesim.isped.u-bordeaux2.fr/SemBiP/ressources/ontoAD.owl). The latter currently includes 5765 concepts linked by 7499 taxonomic relationships and 10,889 non-taxonomic relationships. Among these results, 439 concepts absent from the UMLS were created and 608 new synonymous French terms were added. The proposed method is sufficiently flexible to be applied to other domains.     

The Ontology of Clinical Research (OCRe): An informatics foundation for the science of clinical research

To date, the scientific process for generating, interpreting, and applying knowledge has received less informatics attention than operational processes for conducting clinical studies. The activities of these scientific processes – the science of clinical research – are centered on the study protocol, which is the abstract representation of the scientific design of a clinical study. The Ontology of Clinical Research (OCRe) is an OWL 2 model of the entities and relationships of study design protocols for the purpose of computationally supporting the design and analysis of human studies. OCRe’s modeling is independent of any specific study design or clinical domain. It includes a study design typology and a specialized module called ERGO Annotation for capturing the meaning of eligibility criteria. In this paper, we describe the key informatics use cases of each phase of a study’s scientific lifecycle, present OCRe and the principles behind its modeling, and describe applications of OCRe and associated technologies to a range of clinical research use cases. OCRe captures the central semantics that underlies the scientific processes of clinical research and can serve as an informatics foundation for supporting the entire range of knowledge activities that constitute the science of clinical research.     

The National Institutes of Health’s Biomedical Translational Research Information System (BTRIS): Design,contents, functionality and experience to date

The US National Institutes of Health (NIH) has developed the Biomedical Translational Research Information System (BTRIS) to support researchers’ access to translational and clinical data. BTRIS includes a data repository, a set of programs for loading data from NIH electronic health records and research data management systems, an ontology for coding the disparate data with a single terminology, and a set of user interface tools that provide access to identified data from individual research studies and data across all studies from which individually identifiable data have been removed. This paper reports on unique design elements of the system, progress to date and user experience after five years of development and operation.