J Biomed Inform - An enhanced CRFs-based system for information extraction from radiology reports.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
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{ method(1557) propos(1049) approach(1037) }
{ imag(2675) segment(2577) method(1081) }
{ group(2977) signific(1463) compar(1072) }
{ patient(2315) diseas(1263) diabet(1191) }
{ framework(1458) process(801) describ(734) }
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{ featur(1941) imag(1645) propos(1176) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ data(3008) multipl(1320) sourc(1022) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
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{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

We discuss the problem of performing information extraction from free-text radiology reports via supervised learning. In this task, segments of text (not necessarily coinciding with entire sentences, and possibly crossing sentence boundaries) need to be annotated with tags representing concepts of interest in the radiological domain. In this paper we present two novel approaches to IE for radiology reports: (i) a cascaded, two-stage method based on pipelining two taggers generated via the well known linear-chain conditional random fields (LC-CRFs) learner and (ii) a confidence-weighted ensemble method that combines standard LC-CRFs and the proposed two-stage method. We also report on the use of "positional features", a novel type of feature intended to aid in the automatic annotation of texts in which the instances of a given concept may be hypothesized to systematically occur in specific areas of the text. We present experiments on a dataset of mammography reports in which the proposed ensemble is shown to outperform a traditional, single-stage CRFs system in two different, applicatively interesting scenarios.

Resumo Limpo

discuss problem perform inform extract freetext radiolog report via supervis learn task segment text necessarili coincid entir sentenc possibl cross sentenc boundari need annot tag repres concept interest radiolog domain paper present two novel approach ie radiolog report cascad twostag method base pipelin two tagger generat via well known linearchain condit random field lccrfs learner ii confidenceweight ensembl method combin standard lccrfs propos twostag method also report use posit featur novel type featur intend aid automat annot text instanc given concept may hypothes systemat occur specif area text present experi dataset mammographi report propos ensembl shown outperform tradit singlestag crfs system two differ applic interest scenario

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