Practical development of re-usable terminologies: GALEN-IN-USE and the GALEN Organisation

Rector, A; Rossi, Alessandro; Consorti, Fabrizio; Zanstra, Pieter E.

doi:10.1016/s1386-5056(97)00113-5

Cited by 19 publications

(7 citation statements)

References 10 publications

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“…Note that the approach used in defining the data structure in a CEM model is similar to the dissection approach that is a common practice used in the terminology space for development of re-usable terminologies. The dissection approach was originally used by the GALEN and GALEN-IN-USE projects [25,26]. This kind of post-coordination-based representation would potentially provide the linkage between the data elements defined Second, some data elements are identified as "Common Potential" because these data elements are specified in CDISC templates and do not have corresponding data elements in the CEMs for secondary use while we consider that they have potential to be used in EHR use case.…”

Section: Outstanding Harmonization Issuesmentioning

confidence: 99%

Harmonization of Detailed Clinical Models with Clinical Study Data Standards

Jiang

Evans

Oniki³

et al. 2015

Methods Inf Med

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Background: Data sharing and integration between the clinical research data management system and the electronic health record system remains a challenging issue. To approach the issue, there is emerging interest in utilizing the Detailed Clinical Model (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator awarded Strategic Health IT Advanced Research Projects for normalization (SHARPn) project for normalizing patient data from the electronic health records (EHR).Objective: The objective of the present study is to describe our preliminary efforts toward harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards.Methods: We were focused on three generic domains: demographics, lab tests, and medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs.Results: We have identified a set of data elements that are common to the context of both clinical study and broad secondary use of EHR data and discussed outstanding harmonization issues.Conclusions: We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoper-ability between systems for both clinical study and broad secondary use domains.

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Section: Outstanding Harmonization Issuesmentioning

confidence: 99%

Harmonization of Detailed Clinical Models with Clinical Study Data Standards

Jiang

Evans

Oniki³

et al. 2015

Methods Inf Med

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“…Like in computational systems hierarchies guide the search in memory as well as other processes such as retrieval of information. It is notable that in biomedicine main classifications of diseases and associated terminologies are inherently hierarchical (e.g., GALEN (Rector et al 1998) and SNOMED (Spackman et al 1997)). Pantazi et al notice that from the literature and existing applications, there are two major types of hierarchical approaches to representation: simple hierarchies (total order structures) and multiple hierarchies (partial order structures; Pantazi et al 2007).…”

Section: Prototypical Cases and Memory Organizationmentioning

confidence: 99%

Prototypical Cases for Retrieval, Reuse, and Knowledge Maintenance in Biomedical Case‐based Reasoning

Bichindaritz

2009

Computational Intelligence

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Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Casebased reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge in biomedical fields, which often makes the content of a case base partially obsolete. This article proposes to make use of the concept of prototypical case to ensure that a CBR system would keep update with current research advances in the biomedical field. Prototypical cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of cases, and to serve as bootstrapping a CBR system memory when real cases are not available in sufficient quantity and/or quality. This paper emphasizes the different roles prototypical cases can play in CBR systems, and presents knowledge maintenance as a very important novel role for these prototypical cases.

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“…We included the results of using the simple mapping based on lexical comparison in the PROMPT mapping algorithm to create mappings between the NCI Thesaurus and the Galen ontology [21].…”

Section: Mappings Produced By the Nci Center For Bioinformatics (Ncicb)mentioning

confidence: 99%

Collecting Community-Based Mappings in an Ontology Repository

Noy

Griffith

Musen

2008

Lecture Notes in Computer Science

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Abstract. Several ontology repositories provide access to the growing collection of ontologies on the Semantic Web. Some repositories collect ontologies automatically by crawling the Web; in other repositories, users submit ontologies themselves. In addition to providing search across multiple ontologies, the added value of ontology repositories lies in the metadata that they may contain. This metadata may include information provided by ontology authors, such as ontologies' scope and intended use; feedback provided by users such as their experiences in using the ontologies or reviews of the content; and mapping metadata that relates concepts from different ontologies. In this paper, we focus on the ontology-mapping metadata and on community-based method to collect ontology mappings. More specifically, we develop a model for representing mappings collected from the user community and the metadata associated with the mapping. We use the model to bring together more than 30,000 mappings from 7 sources. We also validate the model by extending BioPortal-a repository of biomedical ontologies that we have developed-to enable users to create single concept-toconcept mappings in its graphical user interface, to upload and download mappings created with other tools, to comment on the mappings and to discuss them, and to visualize the mappings and the corresponding metadata. Ontology Mapping and the Wisdom of the CrowdsAs the number of ontologies available for Semantic Web applications grows, so does the number of ontology repositories that index and organize the ontologies. Some repositories crawl the Web to collect ontologies (e.g., Swoogle [4], Watson [3] and OntoSelect [2]). In other repositories, users submit their ontologies themselves (e.g., the DAML ontology library 1 and SchemaWeb 2 ). These repositories provide a gateway for users and application developers who need to find ontologies to use in their work. In our laboratory, we have developed BioPortal3 -an open repository of biomedical ontologies. Researchers in biomedical informatics submit their ontologies to BioPortal and others can access the ontologies through the BioPortal user interface or through web services. The BioPortal users can browse and search the ontologies, update the ontologies in the repository by uploading new versions, comment on any ontology (or portion of an ontology) in the repository, evaluate it, describe their experience in using the ontology, 1

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Practical development of re-usable terminologies: GALEN-IN-USE and the GALEN Organisation

Cited by 19 publications

References 10 publications

Harmonization of Detailed Clinical Models with Clinical Study Data Standards

Harmonization of Detailed Clinical Models with Clinical Study Data Standards

Prototypical Cases for Retrieval, Reuse, and Knowledge Maintenance in Biomedical Case‐based Reasoning

Collecting Community-Based Mappings in an Ontology Repository

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