J Am Med Inform Assoc - 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ learn(2355) train(1041) set(1003) }
{ record(1888) medic(1808) patient(1693) }
{ research(1085) discuss(1038) issu(1018) }
{ use(976) code(926) identifi(902) }
{ data(1737) use(1416) pattern(1282) }
{ system(1976) rule(880) can(841) }
{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ studi(1119) effect(1106) posit(819) }
{ first(2504) two(1366) second(1323) }
{ analysi(2126) use(1163) compon(1037) }
{ bind(1733) structur(1185) ligand(1036) }
{ motion(1329) object(1292) video(1091) }
{ blood(1257) pressur(1144) flow(957) }
{ model(3480) simul(1196) paramet(876) }
{ sampl(1606) size(1419) use(1276) }
{ can(981) present(881) function(850) }
{ cancer(2502) breast(956) screen(824) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
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{ sequenc(1873) structur(1644) protein(1328) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
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{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
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{ 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) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ 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) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ 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

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.

Resumo Limpo

ibva workshop natur languag process challeng clinic record present three task concept extract task focus extract medic concept patient report assert classif task focus assign assert type medic problem concept relat classif task focus assign relat type hold medic problem test treatment ib va provid annot refer standard corpus three task use refer standard system develop concept extract assert classif relat classif system show machin learn approach augment rulebas system determin concept assert relat depend task rulebas system can either provid input machin learn postprocess output machin learn ensembl classifi inform unlabel data extern knowledg sourc can help train data inadequ

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