J Am Med Inform Assoc - Assessing the role of a medication-indication resource in the treatment relation extraction from clinical text.

Tópicos

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Resumo

JECTIVE: To evaluate the contribution of the MEDication Indication (MEDI) resource and SemRep for identifying treatment relations in clinical text.MATERIALS AND METHODS: We first processed clinical documents with SemRep to extract the Unified Medical Language System (UMLS) concepts and the treatment relations between them. Then, we incorporated MEDI into a simple algorithm that identifies treatment relations between two concepts if they match a medication-indication pair in this resource. For a better coverage, we expanded MEDI using ontology relationships from RxNorm and UMLS Metathesaurus. We also developed two ensemble methods, which combined the predictions of SemRep and the MEDI algorithm. We evaluated our selected methods on two datasets, a Vanderbilt corpus of 6864 discharge summaries and the 2010 Informatics for Integrating Biology and the Bedside (i2b2)/Veteran's Affairs (VA) challenge dataset.RESULTS: The Vanderbilt dataset included 958 manually annotated treatment relations. A double annotation was performed on 25% of relations with high agreement (Cohen's = 0.86). The evaluation consisted of comparing the manual annotated relations with the relations identified by SemRep, the MEDI algorithm, and the two ensemble methods. On the first dataset, the best F1-measure results achieved by the MEDI algorithm and the union of the two resources (78.7 and 80, respectively) were significantly higher than the SemRep results (72.3). On the second dataset, the MEDI algorithm achieved better precision and significantly lower recall values than the best system in the i2b2 challenge. The two systems obtained comparable F1-measure values on the subset of i2b2 relations with both arguments in MEDI.CONCLUSIONS: Both SemRep and MEDI can be used to extract treatment relations from clinical text. Knowledge-based extraction with MEDI outperformed use of SemRep alone, but superior performance was achieved by integrating both systems. The integration of knowledge-based resources such as MEDI into information extraction systems such as SemRep and the i2b2 relation extractors may improve treatment relation extraction from clinical text.

Resumo Limpo

jectiv evalu contribut medic indic medi resourc semrep identifi treatment relat clinic textmateri method first process clinic document semrep extract unifi medic languag system uml concept treatment relat incorpor medi simpl algorithm identifi treatment relat two concept match medicationind pair resourc better coverag expand medi use ontolog relationship rxnorm uml metathesaurus also develop two ensembl method combin predict semrep medi algorithm evalu select method two dataset vanderbilt corpus discharg summari informat integr biolog bedsid ibveteran affair va challeng datasetresult vanderbilt dataset includ manual annot treatment relat doubl annot perform relat high agreement cohen evalu consist compar manual annot relat relat identifi semrep medi algorithm two ensembl method first dataset best fmeasur result achiev medi algorithm union two resourc respect signific higher semrep result second dataset medi algorithm achiev better precis signific lower recal valu best system ib challeng two system obtain compar fmeasur valu subset ib relat argument mediconclus semrep medi can use extract treatment relat clinic text knowledgebas extract medi outperform use semrep alon superior perform achiev integr system integr knowledgebas resourc medi inform extract system semrep ib relat extractor may improv treatment relat extract clinic text

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