J Biomed Inform - Detecting concept relations in clinical text: insights from a state-of-the-art model.

Tópicos

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{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ model(2656) set(1616) predict(1553) }
{ first(2504) two(1366) second(1323) }
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{ blood(1257) pressur(1144) flow(957) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
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{ patient(1821) servic(1111) care(1106) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper addresses an information-extraction problem that aims to identify semantic relations among medical concepts (problems, tests, and treatments) in clinical text. The objectives of the paper are twofold. First, we extend an earlier one-page description (appearing as a part of [5]) of a top-ranked model in the 2010 I2B2 NLP Challenge to a necessary level of details, with the belief that feature design is the most crucial factor to the success of our system and hence deserves a more detailed discussion. We present a precise quantification of the contributions of a wide variety of knowledge sources. In addition, we show the end-to-end results obtained on the noisy output of a top-ranked concept detector, which could help construct a more complete view of the state of the art in the real-world scenario. As the second major objective, we reformulate our models into a composite-kernel framework and present the best result, according to our knowledge, on the same dataset.

Resumo Limpo

paper address informationextract problem aim identifi semant relat among medic concept problem test treatment clinic text object paper twofold first extend earlier onepag descript appear part toprank model ib nlp challeng necessari level detail belief featur design crucial factor success system henc deserv detail discuss present precis quantif contribut wide varieti knowledg sourc addit show endtoend result obtain noisi output toprank concept detector help construct complet view state art realworld scenario second major object reformul model compositekernel framework present best result accord knowledg dataset

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