AMIA Annu Symp Proc - Learning to identify treatment relations in clinical text.

Tópicos

{ record(1888) medic(1808) patient(1693) }
{ extract(1171) text(1153) clinic(932) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ treatment(1704) effect(941) patient(846) }
{ perform(999) metric(946) measur(919) }
{ perform(1367) use(1326) method(1137) }
{ search(2224) databas(1162) retriev(909) }
{ model(3480) simul(1196) paramet(876) }
{ first(2504) two(1366) second(1323) }
{ health(1844) social(1437) communiti(874) }
{ can(774) often(719) complex(702) }
{ take(945) account(800) differ(722) }
{ studi(1410) differ(1259) use(1210) }
{ studi(1119) effect(1106) posit(819) }
{ model(2656) set(1616) predict(1553) }
{ drug(1928) target(777) effect(648) }
{ system(1976) rule(880) can(841) }
{ featur(3375) classif(2383) classifi(1994) }
{ chang(1828) time(1643) increas(1301) }
{ method(1557) propos(1049) approach(1037) }
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{ network(2748) neural(1063) input(814) }
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{ care(1570) inform(1187) nurs(1089) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
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{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
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{ system(1050) medic(1026) inform(1018) }
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{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ 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) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In clinical notes, physicians commonly describe reasons why certain treatments are given. However, this information is not typically available in a computable form. We describe a supervised learning system that is able to predict whether or not a treatment relation exists between any two medical concepts mentioned in clinical notes. To train our prediction model, we manually annotated 958 treatment relations in sentences selected from 6,864 discharge summaries. The features used to indicate the existence of a treatment relation between two medical concepts consisted of lexical and semantic information associated with the two concepts as well as information derived from the MEDication Indication (MEDI) resource and SemRep. The best F1-measure results of our supervised learning system (84.90) were significantly better than the F1-measure results achieved by SemRep (72.34).

Resumo Limpo

clinic note physician common describ reason certain treatment given howev inform typic avail comput form describ supervis learn system abl predict whether treatment relat exist two medic concept mention clinic note train predict model manual annot treatment relat sentenc select discharg summari featur use indic exist treatment relat two medic concept consist lexic semant inform associ two concept well inform deriv medic indic medi resourc semrep best fmeasur result supervis learn system signific better fmeasur result achiev semrep

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