J Am Med Inform Assoc - Automated concept-level information extraction to reduce the need for custom software and rules development.

Tópicos

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

Resumo

JECTIVE: Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval.MATERIALS AND METHODS: A 'learn by example' approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge's concept extraction task provided the data sets and metrics used to evaluate performance.RESULTS: Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks. Discussion With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation.CONCLUSION: Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.

Resumo Limpo

jectiv despit least year promis empir perform clinic natur languag process nlp inform extract system current contribut medic scienc care author address gap reduc need custom softwar rule develop graphic user interfacedriven high generaliz approach conceptlevel retrievalmateri method learn exampl approach combin featur deriv opensourc nlp pipelin opensourc machin learn classifi automat iter evalu topperform configur fourth ibva share task challeng concept extract task provid data set metric use evalu performanceresult top fmeasur score task medic problem treatment test recal lag precis experi precis near task discuss custom task less emspmin endus time configur launch experi averag fmeasur one point behind mean fmeasur entrant competit strong precis score indic potenti appli approach specif clinic inform extract task one best configur support iter approach model creationconclus accept level perform can achiev use fulli autom generaliz approach conceptlevel inform extract describ implement relat document avail download

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