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1.  Evaluating alignment quality between iconic language and reference terminologies using similarity metrics 
Background
Visualization of Concepts in Medicine (VCM) is a compositional iconic language that aims to ease information retrieval in Electronic Health Records (EHR), clinical guidelines or other medical documents. Using VCM language in medical applications requires alignment with medical reference terminologies. Alignment from Medical Subject Headings (MeSH) thesaurus and International Classification of Diseases – tenth revision (ICD10) to VCM are presented here. This study aim was to evaluate alignment quality between VCM and other terminologies using different measures of inter-alignment agreement before integration in EHR.
Methods
For medical literature retrieval purposes and EHR browsing, the MeSH thesaurus and the ICD10, both organized hierarchically, were aligned to VCM language. Some MeSH to VCM alignments were performed automatically but others were performed manually and validated. ICD10 to VCM alignment was entirely manually performed. Inter-alignment agreement was assessed on ICD10 codes and MeSH descriptors, sharing the same Concept Unique Identifiers in the Unified Medical Language System (UMLS). Three metrics were used to compare two VCM icons: binary comparison, crude Dice Similarity Coefficient (DSCcrude), and semantic Dice Similarity Coefficient (DSCsemantic), based on Lin similarity. An analysis of discrepancies was performed.
Results
MeSH to VCM alignment resulted in 10,783 relations: 1,830 of which were manually performed and 8,953 were automatically inherited. ICD10 to VCM alignment led to 19,852 relations. UMLS gathered 1,887 alignments between ICD10 and MeSH. Only 1,606 of them were used for this study. Inter-alignment agreement using only validated MeSH to VCM alignment was 74.2% [70.5-78.0]CI95%, DSCcrude was 0.93 [0.91-0.94]CI95%, and DSCsemantic was 0.96 [0.95-0.96]CI95%. Discrepancy analysis revealed that even if two thirds of errors came from the reviewers, UMLS was nevertheless responsible for one third.
Conclusions
This study has shown strong overall inter-alignment agreement between MeSH to VCM and ICD10 to VCM manual alignments. VCM icons have now been integrated into a guideline search engine (http://www.cismef.org) and a health terminologies portal (http://www.hetop.eu).
doi:10.1186/1472-6947-14-17
PMCID: PMC4007774  PMID: 24618037
Terminology as topic; International classification of diseases; Medical subject headings; Vocabulary; Controlled; Alignment; Iconic language; Compositional language; Semantic distances; Inter-alignment agreement
2.  How are the different specialties represented in the major journals in general medicine? 
Background
General practitioners and medical specialists mainly rely on one "general medical" journal to keep their medical knowledge up to date. Nevertheless, it is not known if these journals display the same overview of the medical knowledge in different specialties. The aims of this study were to measure the relative weight of the different specialties in the major journals of general medicine, to evaluate the trends in these weights over a ten-year period and to compare the journals.
Methods
The 14,091 articles published in The Lancet, the NEJM, the JAMA and the BMJ in 1997, 2002 and 2007 were analyzed. The relative weight of the medical specialities was determined by categorization of all the articles, using a categorization algorithm which inferred the medical specialties relevant to each article MEDLINE file from the MeSH terms used by the indexers of the US National Library of Medicine to describe each article.
Results
The 14,091 articles included in our study were indexed by 22,155 major MeSH terms, which were categorized into 81 different medical specialties. Cardiology and Neurology were in the first 3 specialties in the 4 journals. Five and 15 specialties were systematically ranked in the first 10 and first 20 in the four journals respectively. Among the first 30 specialties, 23 were common to the four journals. For each speciality, the trends over a 10-year period were different from one journal to another, with no consistency and no obvious explanatory factor.
Conclusions
Overall, the representation of many specialties in the four journals in general and internal medicine included in this study may differ, probably due to different editorial policies. Reading only one of these journals may provide a reliable but only partial overview.
doi:10.1186/1472-6947-11-3
PMCID: PMC3031197  PMID: 21255439
3.  A MEDLINE categorization algorithm 
Background
Categorization is designed to enhance resource description by organizing content description so as to enable the reader to grasp quickly and easily what are the main topics discussed in it. The objective of this work is to propose a categorization algorithm to classify a set of scientific articles indexed with the MeSH thesaurus, and in particular those of the MEDLINE bibliographic database. In a large bibliographic database such as MEDLINE, finding materials of particular interest to a specialty group, or relevant to a particular audience, can be difficult. The categorization refines the retrieval of indexed material. In the CISMeF terminology, metaterms can be considered as super-concepts. They were primarily conceived to improve recall in the CISMeF quality-controlled health gateway.
Methods
The MEDLINE categorization algorithm (MCA) is based on semantic links existing between MeSH terms and metaterms on the one hand and between MeSH subheadings and metaterms on the other hand. These links are used to automatically infer a list of metaterms from any MeSH term/subheading indexing. Medical librarians manually select the semantic links.
Results
The MEDLINE categorization algorithm lists the medical specialties relevant to a MEDLINE file by decreasing order of their importance. The MEDLINE categorization algorithm is available on a Web site. It can run on any MEDLINE file in a batch mode. As an example, the top 3 medical specialties for the set of 60 articles published in BioMed Central Medical Informatics & Decision Making, which are currently indexed in MEDLINE are: information science, organization and administration and medical informatics.
Conclusion
We have presented a MEDLINE categorization algorithm in order to classify the medical specialties addressed in any MEDLINE file in the form of a ranked list of relevant specialties. The categorization method introduced in this paper is based on the manual indexing of resources with MeSH (terms/subheadings) pairs by NLM indexers. This algorithm may be used as a new bibliometric tool.
doi:10.1186/1472-6947-6-7
PMCID: PMC1456955  PMID: 16464249
4.  Online clinical reasoning assessment with the Script Concordance test: a feasibility study 
Background
The script concordance (SC) test is an assessment tool that measures capacity to solve ill-defined problems, that is, reasoning in context of uncertainty. This tool has been used up to now mainly in medicine. The purpose of this pilot study is to assess the feasibility of the test delivered on the Web to French urologists.
Methods
The principle of SC test construction and the development of the Web site are described. A secure Web site was created with two sequential modules: (a) The first one for the reference panel (n = 26) with two sub-tasks: to validate the content of the test and to elaborate the scoring system; (b) The second for candidates with different levels of experience in Urology: Board certified urologists, residents, medical students (5 or 6th year). Minimum expected number of participants is 150 for urologists, 100 for residents and 50 for medical students. Each candidate is provided with an individual access code to this Web site. He/she may complete the Script Concordance test several times during his/her curriculum.
Results
The Web site has been operational since April 2004. The reference panel validated the test in June of the same year during the annual seminar of the French Society of Urology. The Web site is available for the candidates since September 2004. In six months, 80% of the target figure for the urologists, 68% of the target figure for the residents and 20% of the target figure for the student passed the test online. During these six months, no technical problem was encountered.
Conclusion
The feasibility of the web-based SC test is successful as two-thirds of the expected number of participants was included within six months. Psychometric properties (validity, reliability) of the test will be evaluated on a large scale (N = 300). If positive, educational impact of this assessment tool will be useful to help urologists during their curriculum for the acquisition of clinical reasoning skills, which is crucial for professional competence.
doi:10.1186/1472-6947-5-18
PMCID: PMC1184080  PMID: 15967034

Results 1-4 (4)