J Biomed Inform - Using an ensemble system to improve concept extraction from clinical records.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ record(1888) medic(1808) patient(1693) }
{ perform(1367) use(1326) method(1137) }
{ data(1714) softwar(1251) tool(1186) }
{ research(1218) medic(880) student(794) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ error(1145) method(1030) estim(1020) }
{ perform(999) metric(946) measur(919) }
{ age(1611) year(1155) adult(843) }
{ can(981) present(881) function(850) }
{ detect(2391) sensit(1101) algorithm(908) }
{ can(774) often(719) complex(702) }
{ featur(3375) classif(2383) classifi(1994) }
{ treatment(1704) effect(941) patient(846) }
{ algorithm(1844) comput(1787) effici(935) }
{ health(3367) inform(1360) care(1135) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ take(945) account(800) differ(722) }
{ problem(2511) optim(1539) algorithm(950) }
{ design(1359) user(1324) use(1319) }
{ studi(1410) differ(1259) use(1210) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ model(3480) simul(1196) paramet(876) }
{ state(1844) use(1261) util(961) }
{ signal(2180) analysi(812) frequenc(800) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ high(1669) rate(1365) level(1280) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Recognition of medical concepts is a basic step in information extraction from clinical records. We wished to improve on the performance of a variety of concept recognition systems by combining their individual results. We selected two dictionary-based systems and five statistical-based systems that were trained to annotate medical problems, tests, and treatments in clinical records. Manually annotated clinical records for training and testing were made available through the 2010 i2b2/VA (Informatics for Integrating Biology and the Bedside) challenge. Results of individual systems were combined by a simple voting scheme. The statistical systems were trained on a set of 349 records. Performance (precision, recall, F-score) was assessed on a test set of 477 records, using varying voting thresholds. The combined annotation system achieved a best F-score of 82.2% (recall 81.2%, precision 83.3%) on the test set, a score that ranks third among 22 participants in the i2b2/VA concept annotation task. The ensemble system had better precision and recall than any of the individual systems, yielding an F-score that is 4.6% point higher than the best single system. Changing the voting threshold offered a simple way to obtain a system with high precision (and moderate recall) or one with high recall (and moderate precision). The ensemble-based approach is straightforward and allows the balancing of precision versus recall of the combined system. The ensemble system is freely available and can easily be extended, integrated in other systems, and retrained.

Resumo Limpo

recognit medic concept basic step inform extract clinic record wish improv perform varieti concept recognit system combin individu result select two dictionarybas system five statisticalbas system train annot medic problem test treatment clinic record manual annot clinic record train test made avail ibva informat integr biolog bedsid challeng result individu system combin simpl vote scheme statist system train set record perform precis recal fscore assess test set record use vari vote threshold combin annot system achiev best fscore recal precis test set score rank third among particip ibva concept annot task ensembl system better precis recal individu system yield fscore point higher best singl system chang vote threshold offer simpl way obtain system high precis moder recal one high recal moder precis ensemblebas approach straightforward allow balanc precis versus recal combin system ensembl system freeli avail can easili extend integr system retrain

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