liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 16 of 16
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Balkanyi, Laszlo
    et al.
    European Centre for Disease Prevention and Control, Stockholm, Sweden.
    Schulz, Stefan
    Medizinische Universität Graz, Austria and Freiburg University Medical Center, Freiburg, Germany.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Bodenreider, Olivier
    National Library of Medicine, Bethsheda, USA.
    Medical concept representation: the years beyond 2000.2013In: Proceedings of Studies in Health Technology & Informatics, vol. 192, IOS Press, 2013, Vol. 192, p. 1011-1011Conference paper (Refereed)
    Abstract [en]

    This work aims at understanding the state of the art in the broad contextual research area of "medical concept representation". Our data support the general understanding that the focus of research has moved toward medical ontologies, which we interpret as a paradigm shift. Both the opinion of socially active groups of researchers and changes in bibliometric data since 1988 support this opinion. Socially active researchers mention the OBO foundry, SNOMED CT, and the UMLS as anchor activities.

  • 2.
    Cornet, Ronald
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dentler, Kathrin
    Dept. of Computer Science, VU University Amsterdam, The Netherlands and Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands.
    Redundant Elements in SNOMED CT Concept Definitions2013In: proceedings of AIME 2013, Lecture Notes in ComputerScience 2013, Vol. 7885 / [ed] Peek, Niels, Marín Morales, Roque Luis, Peleg, Mor, Springer , 2013, p. 186-195Conference paper (Refereed)
    Abstract [en]

    While redundant elements in SNOMED CT concept definitions are harmless from a logical point of view, they unnecessarily make concept definitions of typically large ontologies such as SNOMED CT hard to construct and to maintain. In this paper, we apply a fully automated method to detect intra-axiom redundancies in SNOMED CT. We systematically analyse the completeness and soundness of the results of our method by examining the identified redundant elements. In absence of a gold standard, we check whether our method identifies concepts that are likely to contain redundant elements because they become equivalent to their stated subsumer when they are replaced by a fully defined concept with the same definition. To evaluate soundness, we remove all identified redundancies, and test whether the logical closure is preserved by comparing the concept hierarchy to the one of the official SNOMED CT distribution. We found that 35,010 of the 296,433 SNOMED CT concepts (12%) contain redundant elements in their definitions, and that the results of our method are sound and complete with respect to our partial evaluation. We recommend to free the stated form from these redundancies. In future, knowledge modellers should be supported by being pointed to newly introduced redundancies.

  • 3.
    Cornet, Ronald
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Amsterdam, The Netherlands.
    Nyström, Mikael
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Karlsson, Daniel
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    User-Directed Coordination in SNOMED CT2013In: MedInfo 2013: Proceedings of the 14th World Congress on Medical and Health Informatics / [ed] Lehmann, C.U., Ammenwerth, E., Nøhr, C., Amsterdam: IOS Press, 2013, p. 72-76Conference paper (Refereed)
    Abstract [en]

    The possibility of post-coordination of SNOMED CT concepts, especially by clinical users, is both an asset and a challenge for SNOMED CT implementation. To get insight in the applicability of post-coordination, we analyzed scenarios for user-directed coordination that are described in the documentation of SNOMED CT. The analyses were based on experiences from previous and ongoing research and implementation work, including national mapping projects, and investigations on collection of data for multiple uses. These scenarios show various usability and representation problems: high number of relationships for refinement and qualification, improper options for refinement, incorrect formal definitions, and lack of support for applying editorial rules. Improved user-directed coordination in SNOMED CT in real practice requires advanced sanctioning, increased consistency of definitions of concepts in SNOMED CT, and real-time analysis of the post-coordinate expression.

  • 4.
    Dentler, Kathrin
    et al.
    Vrije University of Amsterdam, Netherlands; University of Amsterdam, Netherlands.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. University of Amsterdam, Netherlands.
    Intra-axiom redundancies in SNOMED CT2015In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 65, no 1, p. 29-34Article in journal (Refereed)
    Abstract [en]

    Objective: Intra-axiom redundancies are elements of concept definitions that are redundant as they are entailed by other elements of the concept definition. While such redundancies are harmless from a logical point of view, they make concept definitions hard to maintain, and they might lead to content-related problems when concepts evolve. The objective of this study is to develop a fully automated method to detect intra-axiom redundancies in OWL 2 EL and apply it to SNOMED Clinical Terms (SNOMED CT). Materials and methods: We developed a software program in which we implemented, adapted and extended readily existing rules for redundancy elimination. With this, we analysed occurence of redundancy in 11 releases of SNOMED CT(January 2009 to January 2014). We used the ELK reasoner to classify SNOMED CT, and Pellet for explanation of equivalence. We analysed the completeness and soundness of the results by an in-depth examination of the identified redundant elements in the July 2012 release of SNOMED CT. To determine if concepts with redundant elements lead to maintenance issues, we analysed a small sample of solved redundancies. Results: Analyses showed that the amount of redundantly defined concepts in SNOMED CT is consistently around 35,000. In the July 2012 version of SNOMED CT, 35,010(12%) of the 296,433 concepts contained redundant elements in their definitions. The results of applying our method are sound and complete with respect to our evaluation. Analysis of solved redundancies suggests that redundancies in concept definitions lead to inadequate maintenance of SNOMED CT. Conclusions: Our analysis revealed that redundant elements are continuously introduced and removed, and that redundant elements may be overlooked when concept definitions are corrected. Applying our redundancy detection method to remove intra-axiom redundancies from the stated form of SNOMED CT and to point knowledge modellers to newly introduced redundancies can support creating and maintaining a redundancy-free version of SNOMED CT. (C) 2014 Elsevier B.V. All rights reserved.

  • 5.
    Dentler, Kathrin
    et al.
    Vrije University of Amsterdam, Netherlands University of Amsterdam, Netherlands .
    Numans, Mattijs E.
    University of Medical Centre Utrecht, Netherlands Vrije University of Amsterdam, Netherlands .
    ten Teije, Annette
    Vrije University of Amsterdam, Netherlands .
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Amsterdam, Netherlands.
    de Keizer, Nicolette F.
    University of Amsterdam, Netherlands .
    Formalization and computation of quality measures based on electronic medical records2014In: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, Vol. 21, no 2, p. 285-291Article in journal (Refereed)
    Abstract [en]

    Objective Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizablethat is, applicable to a large set of heterogeneous measures of different types and from various domains. Materials and methods We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Results Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. Conclusions The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practice.

  • 6.
    Dentler, Kathrin
    et al.
    Dept. of Computer Science, VU University Amsterdam, The Netherlands and Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands.
    Ten Teije, Annette
    Dept. of Computer Science, VU University Amsterdam, The Netherlands.
    de Keizer, Nicolette
    Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Barriers to the reuse of routinely recorded clinical data: a field report2013In: Proceedings of Studies in Health Technology & Informatics, vol.192, IOS Press, 2013, Vol. 192, p. 313-317Conference paper (Refereed)
    Abstract [en]

    Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery. We categorised the barriers that we encountered in the scope of this project according to an existing framework, and provide recommendations on how to prevent or surmount these barriers. Assuming that our case is not unique, these recommendations might be applicable for the design, evaluation and optimisation of Electronic Health Records.

  • 7.
    Joukes, Erik
    et al.
    University of Amsterdam, Netherlands.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. University of Amsterdam, Netherlands.
    de Bruijne, Martine C.
    Vrije University of Amsterdam, Netherlands.
    de Keizer, Nicolette F.
    University of Amsterdam, Netherlands.
    Eliciting end-user expectations to guide the implementation process of a new electronic health record: A case study using concept mapping2016In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 87, p. 111-117Article in journal (Refereed)
    Abstract [en]

    Objective: To evaluate the usability of concept mapping to elicit the expectations of healthcare professionals regarding the implementation of a new electronic health record (EHR). These expectations need to be taken into account during the implementation process to maximize the chance of success of the EHR. Setting: Two university hospitals in Amsterdam, The Netherlands, in the preparation phase of jointly implementing a new EHR. During this study the hospitals had different methods of documenting patient information (legacy EHR vs. paper-based records). Method: Concept mapping was used to determine and classify the expectations of healthcare professionals regarding the implementation of a new EHR. A multidisciplinary group of 46 healthcare professionals from both university hospitals participated in this study. Expectations were elicited in focus groups, their relevance and feasibility were assessed through a web-questionnaire. Nonmetric multidimensional scaling and clustering methods were used to identify clusters of expectations. Results: We found nine clusters of expectations, each covering an important topic to enable the healthcare professionals to work properly with the new EHR once implemented: usability, data use and reuse, facility conditions, data registration, support, training, internal communication, patients, and collaboration. Average importance and feasibility of each of the clusters was high. Conclusion: Concept mapping is an effective method to find topics that, according to healthcare professionals, are important to consider during the implementation of a new EHR. The method helps to combine the input of a large group of stakeholders at limited efforts. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

  • 8.
    Joukes, Erik
    et al.
    Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands.
    de Keizer, Nicolette
    Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Composite Quality of Care Scores, Electronic Health Record Maturity Models, and their Associations; Preliminary Literature Review Results.2013In: Proceedings of Studies in Health Technology & Informatics, vol. 192, 2013, Vol. 192, p. 981-981Conference paper (Refereed)
    Abstract [en]

    To accurately assess the association between the use of EHR systems and the quality of healthcare we need (composite) measures for quality of healthcare, and a model to measure the maturity of the EHR. This Medline-based literature study therefore focussed on three topics; (1) methods to compose a measure for quality of care based on individual quality indicators (QI), (2) models to measure EHR maturity, and (3) the association between the former two. Composite quality is most often measured using opportunity-based scores, maturity is measured in functionalities or levels. EHR maturity measures are not used extensively in biomedical literature. Most studies found a positive association between EHR use and the quality of care but almost none of them differentiate in maturity of EHR which hampers firm conclusions about this relation.

  • 9.
    Lee, Dennis
    et al.
    University of Victoria, BC, Canada.
    de Keizer, Nicolette
    University of Amsterdam, The Netherlands .
    Lau, Francis
    University of Victoria, BC, Canada.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Literature review of SNOMED CT use2014In: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, Vol. 21, no E1, p. E11-E19Article, review/survey (Refereed)
    Abstract [en]

    OBJECTIVE: The aim of this paper is to report on the use of the systematised nomenclature of medicine clinical terms (SNOMED CT) by providing an overview of published papers.

    METHODS: Published papers on SNOMED CT between 2001 and 2012 were identified using PubMed and Embase databases using the keywords 'systematised nomenclature of medicine' and 'SNOMED CT'. For each paper the following characteristics were retrieved: SNOMED CT focus category (ie, indeterminate, theoretical, pre-development/design, implementation and evaluation/commodity), usage category (eg, prospective content coverage, used to classify or code in a study), medical domain and country.

    RESULTS: Our search strategy identified 488 papers. A comparison between the papers published between 2001-6 and 2007-12 showed an increase in every SNOMED CT focus category. The number of papers classified as 'theoretical' increased from 46 to 78, 'pre-development/design' increased from 61 to 173 and 'implementation' increased from 10 to 34. Papers classified as 'evaluation/commodity' only started to appear from 2010.

    CONCLUSIONS: The majority of studies focused on 'theoretical' and 'pre-development/design'. This is still encouraging as SNOMED CT is being harmonized with other standardized terminologies and is being evaluated to determine the content coverage of local terms, which is usually one of the first steps towards adoption. Most implementations are not published in the scientific literature, requiring a look beyond the scientific literature to gain insights into SNOMED CT implementations.

  • 10.
    Oluoch, Tom
    et al.
    US Centers for Disease Control and Prevention/DGHA, Nairobi, Kenya.
    de Keizer, Nicolette
    Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands.
    Kwaro, Daniel
    Kenya Medical Research Institute/CDC collaborative program, Kisumu, Kenya.
    Wattoyi, Irene
    Kenya Medical Research Institute/CDC collaborative program, Kisumu, Kenya.
    Okeyo, Nicky
    Kenya Medical Research Institute/CDC collaborative program, Kisumu, Kenya.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Inconsistencies between Recorded Opportunistic Infections and WHO HIV Staging in Western Kenya.2013In: Proceedings of Studies in Health Technology & Informatics, vol. 192, IOS Press, 2013, Vol. 192, p. 1139-1139Conference paper (Refereed)
    Abstract [en]

    Opportunistic infections (OIs) are the main cause of morbidity and mortality among patients with HIV in developing countries. It is therefore critical that accurate diagnoses are made and that they are correctly recorded and managed. We reviewed 200 randomly selected records of clinical encounters with HIV infected pregnant women attending the ante-natal care (ANC) clinic in July 2012 at the Jaramogi Oginga Odinga Teaching and Referral Hospital in Kenya. None of the clients in WHO stage 4 and 2.8% of those in WHO stage 3 had a new OI diagnosis recorded during the clinical encounter. This data suggests current under-recording of OIs and the inconsistency between WHO staging and OI diagnosis. Structured methods such as SNOMED CT have the potential to improve complete and accurate recording of OIs which, in turn, enable automatedand accurate WHO staging.

  • 11.
    Oluoch, Tom
    et al.
    US Centre Disease Control and Prevent, Kenya.
    de Keizer, Nicolette
    University of Amsterdam, Netherlands.
    Langat, Patrick
    Kenya Govt Medical Research Centre, Kenya.
    Alaska, Irene
    Kenya Govt Medical Research Centre, Kenya.
    Ochieng, Kenneth
    Kenya Govt Medical Research Centre, Kenya.
    Okeyo, Nicky
    Kenya Govt Medical Research Centre, Kenya.
    Kwaro, Daniel
    Kenya Govt Medical Research Centre, Kenya.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. University of Amsterdam, Netherlands.
    A structured approach to recording AIDS-defining illnesses in Kenya: A SNOMED CT based solution2015In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 56, p. 387-394Article in journal (Refereed)
    Abstract [en]

    Introduction: Several studies conducted in sub-Saharan Africa (SSA) have shown that routine clinical data in HIV clinics often have errors. Lack of structured and coded documentation of diagnosis of AIDS defining illnesses (ADIs) can compromise data quality and decisions made on clinical care. Methods: We used a structured framework to derive a reference set of concepts and terms used to describe ADIs. The four sources used were: (i) CDC/Accenture list of opportunistic infections, (ii) SNOMED Clinical Terms (SNOMED CT), (iii) Focus Group Discussion (FGD) among clinicians and nurses attending to patients at a referral provincial hospital in western Kenya, and (iv) chart abstraction from the Maternal Child Health (MCH) and HIV clinics at the same hospital. Using the January 2014 release of SNOMED CT, concepts were retrieved that matched terms abstracted from approach iii and iv, and the content coverage assessed. Post-coordination matching was applied when needed. Results: The final reference set had 1054 unique ADI concepts which were described by 1860 unique terms. Content coverage of SNOMED CT was high (99.9% with pre-coordinated concepts; 100% with post-coordination). The resulting reference set for ADIs was implemented as the interface terminology on OpenMRS data entry forms. Conclusion: Different sources demonstrate complementarity in the collection of concepts and terms for an interface terminology. SNOMED CT provides a high coverage in the domain of ADIs. Further work is needed to evaluate the effect of the interface terminology on data quality and quality of care.

  • 12.
    Oluoch, Tom
    et al.
    US Centre Disease Control and Prevent CDC, Kenya.
    Katana, Abraham
    US Centre Disease Control and Prevent CDC, Kenya.
    Kwaro, Daniel
    Kenya Govt Medical Research Centre, Kenya.
    Santas, Xenophon
    US Centre Disease Control and Prevent CDC, GA USA.
    Langat, Patrick
    Kenya Govt Medical Research Centre, Kenya.
    Mwalili, Samuel
    US Centre Disease Control and Prevent CDC, Kenya.
    Muthusi, Kimeu
    University of Calif San Francisco, Kenya.
    Okeyo, Nicky
    Kenya Govt Medical Research Centre, Kenya.
    Ojwang, James K.
    US Centre Disease Control and Prevent CDC, Kenya.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. University of Amsterdam, Netherlands.
    Abu-Hanna, Ameen
    University of Amsterdam, Netherlands.
    de Keizer, Nicolette
    University of Amsterdam, Netherlands.
    Effect of a clinical decision support system on early action on immunological treatment failure in patients with HIV in Kenya: a cluster randomised controlled trial2016In: LANCET HIV, ISSN 2352-3018, Vol. 3, no 2, p. E76-E84Article in journal (Refereed)
    Abstract [en]

    Background A clinical decision support system (CDSS) is a computer program that applies a set of rules to data stored in electronic health records to off er actionable recommendations. We aimed to establish whether a CDSS that supports detection of immunological treatment failure among patients with HIV taking antiretroviral therapy (ART) would improve appropriate and timely action. Methods We did this prospective, cluster randomised controlled trial in adults and children (aged >= 18 months) who were eligible for, and receiving, ART at HIV clinics in Siaya County, western Kenya. Health facilities were randomly assigned (1: 1), via block randomisation (block size of two) with a computer-generated random number sequence, to use electronic health records either alone (control) or with CDSS (intervention). Facilities were matched by type and by number of patients enrolled in HIV care. The primary outcome measure was the difference between groups in the proportion of patients who experienced immunological treatment failure and had a documented clinical action. We used generalised linear mixed models with random effects to analyse clustered data. This trial is registered with ClinicalTrials.gov, number NCT01634802. Findings Between Sept 1, 2012, and Jan 31, 2014, 13 clinics, comprising 41 062 patients, were randomly assigned to the control group (n=6) or the intervention group (n=7). Data collection at each site took 12 months. Among patients eligible for ART, 10 358 (99%) of 10 478 patients were receiving ART at control sites and 10 991 (99%) of 11 028 patients were receiving ART at intervention sites. Of these patients, 1125 (11%) in the control group and 1342 (12%) in the intervention group had immunological treatment failure, of whom 332 (30%) and 727 (54%), respectively, received appropriate action. The likelihood of clinicians taking appropriate action on treatment failure was higher with CDSS alerts than with no decision support system (adjusted odds ratio 3.18, 95% CI 1.02-9.87). Interpretation CDSS significantly improved the likelihood of appropriate and timely action on immunological treatment failure. We expect our findings will be generalisable to virological monitoring of patients with HIV receiving ART once countries implement the 2015 WHO recommendation to scale up viral load monitoring.

  • 13.
    Rosenbeck Goeg, Kirstine
    et al.
    Aalborg University, Denmark.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. University of Amsterdam, Netherlands.
    Kjaer Andersen, Stig
    Aalborg University, Denmark.
    Clustering clinical models from local electronic health records based on semantic similarity2015In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 54, p. 294-304Article in journal (Refereed)
    Abstract [en]

    Background: Clinical models in electronic health records are typically expressed as templates which support the multiple clinical workflows in which the system is used. The templates are often designed using local rather than standard information models and terminology, which hinders semantic interoperability. Semantic challenges can be solved by harmonizing and standardizing clinical models. However, methods supporting harmonization based on existing clinical models are lacking. One approach is to explore semantic similarity estimation as a basis of an analytical framework. Therefore, the aim of this study is to develop and apply methods for intrinsic similarity-estimation based analysis that can compare and give an overview of multiple clinical models. Method: For a similarity estimate to be intrinsic it should be based on an established ontology, for which SNOMED CT was chosen. In this study, Lin similarity estimates and Sokal and Sneath similarity estimates were used together with two aggregation techniques (average and best-match-average respectively) resulting in a total of four methods. The similarity estimations are used to hierarchically cluster templates. The test material consists of templates from Danish and Swedish EHR systems. The test material was used to evaluate how the four different methods perform. Result and discussion: The best-match-average aggregation technique performed better in terms of clustering similar templates than the average aggregation technique. No difference could be seen in terms of the choice of similarity estimate in this study, but the finding may be different for other datasets. The dendrograms resulting from the hierarchical clustering gave an overview of the templates and a basis of further analysis. Conclusion: Hierarchical clustering of templates based on SNOMED CT and semantic similarity estimation with best-match-average aggregation technique can be used for comparison and summarization of multiple templates. Consequently, it can provide a valuable tool for harmonization and standardization of clinical models.

  • 14.
    Schulz, Stefan
    et al.
    Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
    Balkanyi, Laszlo
    European Centre for Disease Prevention and Control, Stockholm, Sweden.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Bodenreider, Olivier
    National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
    From Concept Representations to Ontologies: A Paradigm Shift in Health Informatics?2013In: Healthcare informatics research, ISSN 2093-3681, Vol. 19, no 4, p. 235-242Article in journal (Refereed)
    Abstract [en]

    OBJECTIVES: This work aims at uncovering challenges in biomedical knowledge representation research by providing an understanding of what was historically called "medical concept representation" and used as the name for a working group of the International Medical Informatics Association.

    METHODS: Bibliometrics, text mining, and a social media survey compare the research done in this area between two periods, before and after 2000.

    RESULTS: Both the opinion of socially active groups of researchers and the interpretation of bibliometric data since 1988 suggest that the focus of research has moved from "medical concept representation" to "medical ontologies".

    CONCLUSIONS: It remains debatable whether the observed change amounts to a paradigm shift or whether it simply reflects changes in naming, following the natural evolution of ontology research and engineering activities in the 1990s. The availability of powerful tools to handle ontologies devoted to certain areas of biomedicine has not resulted in a large-scale breakthrough beyond advances in basic research.

  • 15.
    Schulz, Stefan
    et al.
    Medical University of Graz, Austria.
    Martínez-Costa, Catalina
    Medical University of Graz, Austria.
    Karlsson, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Academic Medical Center, Amsterdam, The Netherlands.
    Brochhausen, Mathias
    University of Arkansas for Medical Sciences, U.S..
    Rector, Alan
    University of Manchester, U.K..
    An Ontological Analysis of Reference in Health Record Statements2014In: Formal Ontology in Information Systems / [ed] Pawel Garbacz, Oliver Kutz, Amsterdam: IOS Press, 2014, Vol. 267, p. 289-302Conference paper (Refereed)
    Abstract [en]

    The relation between an information entity and its referent can be described as a second-order statement, as long as the referent is a type. This is typical for medical discourse such as diagnostic statements in electronic health records (EHRs), which often express hypotheses or probability assertions about the existence of an instance of, e.g. a disease type. This paper presents several approximations using description logics and a query language, the entailments of which are checked against a reference standard. Their pros and cons are discussed in the light of formal ontology and logic.

  • 16.
    Scott, Philip J.
    et al.
    University of Portsmouth, UK.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Academic Medical Center, University of Amsterdam, Netherland.
    McCowan, Colin
    University of Glasgow, UK.
    Peek, Niels
    University of Manchester, UK.
    Fraccaro, Paolo
    University of Manchester, UK.
    Geifman, Nophar
    University of Manchester, UK.
    Gude, Wouter T
    Academic Medical Center, University of Amsterdam, Netherlands.
    Hulme, William
    University of Manchester, UK.
    Martin, Glen P.
    University of Manchester, UK.
    Williams, Richard
    University of Manchester, UK.
    Informatics for Health 2017: Advancing both science and practice2017Conference paper (Other academic)
    Abstract [en]

    The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress.

1 - 16 of 16
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf