J Am Med Inform Assoc - Functional evaluation of out-of-the-box text-mining tools for data-mining tasks.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ method(2212) result(1239) propos(1039) }
{ featur(3375) classif(2383) classifi(1994) }
{ error(1145) method(1030) estim(1020) }
{ spatial(1525) area(1432) region(1030) }
{ learn(2355) train(1041) set(1003) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ first(2504) two(1366) second(1323) }
{ analysi(2126) use(1163) compon(1037) }
{ system(1976) rule(880) can(841) }
{ clinic(1479) use(1117) guidelin(835) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ drug(1928) target(777) effect(648) }
{ decis(3086) make(1611) patient(1517) }
{ network(2748) neural(1063) input(814) }
{ problem(2511) optim(1539) algorithm(950) }
{ care(1570) inform(1187) nurs(1089) }
{ data(3963) clinic(1234) research(1004) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ research(1218) medic(880) student(794) }
{ can(981) present(881) function(850) }
{ imag(1057) registr(996) error(939) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ search(2224) databas(1162) retriev(909) }
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{ studi(1119) effect(1106) posit(819) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
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{ motion(1329) object(1292) video(1091) }
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{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
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{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
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{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

JECTIVE: The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug-drug interactions, and learning used-to-treat relationships between drugs and indications.MATERIALS: We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks.RESULTS: There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets.CONCLUSIONS: For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.

Resumo Limpo

jectiv tradeoff speed simplic dictionarybas term recognit richer linguist inform provid advanc natur languag process nlp area activ discuss clinic informat paper quantifi tradeoff among text process system make differ tradeoff speed linguist understand test type system three clinic research task phase iv safeti profil drug learn advers drugdrug interact learn usedtotreat relationship drug indicationsmateri first benchmark accuraci ncbo annot reveal manual annot public avail dataset ib obes challeng appli ncbo annot reveal million clinic note stanford translat research integr databas environ stride use result data three research tasksresult signific differ use ncbo annot reveal result three research task use larg dataset one subtask reveal achiev higher sensit smaller datasetsconclus varieti task employ simpl term recognit method instead advanc nlp method result littl impact accuraci use larg dataset simpler dictionarybas method advantag scale well larg dataset promot use simpl dictionarybas method popul level analys can advanc adopt nlp practic

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