J Biomed Inform - Clustering clinical models from local electronic health records based on semantic similarity.

Tópicos

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{ method(1219) similar(1157) match(930) }
{ clinic(1479) use(1117) guidelin(835) }
{ data(3008) multipl(1320) sourc(1022) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
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{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ drug(1928) target(777) effect(648) }
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{ activ(1452) weight(1219) physic(1104) }

Resumo

CKGROUND: 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.

Resumo Limpo

ckground clinic model electron health record typic express templat support multipl clinic workflow system use templat often design use local rather standard inform model terminolog hinder semant interoper semant challeng can solv harmon standard clinic model howev method support harmon base exist clinic model lack one approach explor semant similar estim basi analyt framework therefor aim studi develop appli method intrins similarityestim base analysi can compar give overview multipl clinic modelsmethod similar estim intrins base establish ontolog snome ct chosen studi lin similar estim sokal sneath similar estim use togeth two aggreg techniqu averag bestmatchaverag respect result total four method similar estim use hierarch cluster templat test materi consist templat danish swedish ehr system test materi use evalu four differ method performresult discuss bestmatchaverag aggreg techniqu perform better term cluster similar templat averag aggreg techniqu differ seen term choic similar estim studi find may differ dataset dendrogram result hierarch cluster gave overview templat basi analysisconclus hierarch cluster templat base snome ct semant similar estim bestmatchaverag aggreg techniqu can use comparison summar multipl templat consequ can provid valuabl tool harmon standard clinic model

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