J Am Med Inform Assoc - Evaluating the state of the art in coreference resolution for electronic medical records.

Tópicos

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{ medic(1828) order(1363) alert(1069) }
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{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: The fifth i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records conducted a systematic review on resolution of noun phrase coreference in medical records. Informatics for Integrating Biology and the Bedside (i2b2) and the Veterans Affair (VA) Consortium for Healthcare Informatics Research (CHIR) partnered to organize the coreference challenge. They provided the research community with two corpora of medical records for the development and evaluation of the coreference resolution systems. These corpora contained various record types (ie, discharge summaries, pathology reports) from multiple institutions.METHODS: The coreference challenge provided the community with two annotated ground truth corpora and evaluated systems on coreference resolution in two ways: first, it evaluated systems for their ability to identify mentions of concepts and to link together those mentions. Second, it evaluated the ability of the systems to link together ground truth mentions that refer to the same entity. Twenty teams representing 29 organizations and nine countries participated in the coreference challenge.RESULTS: The teams' system submissions showed that machine-learning and rule-based approaches worked best when augmented with external knowledge sources and coreference clues extracted from document structure. The systems performed better in coreference resolution when provided with ground truth mentions. Overall, the systems struggled in solving coreference resolution for cases that required domain knowledge.

Resumo Limpo

ckground fifth ibva workshop natur languag process challeng clinic record conduct systemat review resolut noun phrase corefer medic record informat integr biolog bedsid ib veteran affair va consortium healthcar informat research chir partner organ corefer challeng provid research communiti two corpora medic record develop evalu corefer resolut system corpora contain various record type ie discharg summari patholog report multipl institutionsmethod corefer challeng provid communiti two annot ground truth corpora evalu system corefer resolut two way first evalu system abil identifi mention concept link togeth mention second evalu abil system link togeth ground truth mention refer entiti twenti team repres organ nine countri particip corefer challengeresult team system submiss show machinelearn rulebas approach work best augment extern knowledg sourc corefer clue extract document structur system perform better corefer resolut provid ground truth mention overal system struggl solv corefer resolut case requir domain knowledg

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