Artif Intell Med - Semantic relations for problem-oriented medical records.

Tópicos

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Resumo

JECTIVE: We describe semantic relation (SR) classification on medical discharge summaries. We focus on relations targeted to the creation of problem-oriented records. Thus, we define relations that involve the medical problems of patients.METHODS AND MATERIALS: We represent patients' medical problems with their diseases and symptoms. We study the relations of patients' problems with each other and with concepts that are identified as tests and treatments. We present an SR classifier that studies a corpus of patient records one sentence at a time. For all pairs of concepts that appear in a sentence, this SR classifier determines the relations between them. In doing so, the SR classifier takes advantage of surface, lexical, and syntactic features and uses these features as input to a support vector machine. We apply our SR classifier to two sets of medical discharge summaries, one obtained from the Beth Israel-Deaconess Medical Center (BIDMC), Boston, MA and the other from Partners Healthcare, Boston, MA.RESULTS: On the BIDMC corpus, our SR classifier achieves micro-averaged F-measures that range from 74% to 95% on the various relation types. On the Partners corpus, the micro-averaged F-measures on the various relation types range from 68% to 91%. Our experiments show that lexical features (in particular, tokens that occur between candidate concepts, which we refer to as inter-concept tokens) are very informative for relation classification in medical discharge summaries. Using only the inter-concept tokens in the corpus, our SR classifier can recognize 84% of the relations in the BIDMC corpus and 72% of the relations in the Partners corpus.CONCLUSION: These results are promising for semantic indexing of medical records. They imply that we can take advantage of lexical patterns in discharge summaries for relation classification at a sentence level.

Resumo Limpo

jectiv describ semant relat sr classif medic discharg summari focus relat target creation problemori record thus defin relat involv medic problem patientsmethod materi repres patient medic problem diseas symptom studi relat patient problem concept identifi test treatment present sr classifi studi corpus patient record one sentenc time pair concept appear sentenc sr classifi determin relat sr classifi take advantag surfac lexic syntact featur use featur input support vector machin appli sr classifi two set medic discharg summari one obtain beth israeldeaco medic center bidmc boston ma partner healthcar boston maresult bidmc corpus sr classifi achiev microaverag fmeasur rang various relat type partner corpus microaverag fmeasur various relat type rang experi show lexic featur particular token occur candid concept refer interconcept token inform relat classif medic discharg summari use interconcept token corpus sr classifi can recogn relat bidmc corpus relat partner corpusconclus result promis semant index medic record impli can take advantag lexic pattern discharg summari relat classif sentenc level

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