J Am Med Inform Assoc - Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ featur(1941) imag(1645) propos(1176) }
{ record(1888) medic(1808) patient(1693) }
{ group(2977) signific(1463) compar(1072) }
{ learn(2355) train(1041) set(1003) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ featur(3375) classif(2383) classifi(1994) }
{ problem(2511) optim(1539) algorithm(950) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ activ(1138) subject(705) human(624) }
{ analysi(2126) use(1163) compon(1037) }
{ implement(1333) system(1263) develop(1122) }
{ process(1125) use(805) approach(778) }
{ inform(2794) health(2639) internet(1427) }
{ method(1219) similar(1157) match(930) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ chang(1828) time(1643) increas(1301) }
{ data(1714) softwar(1251) tool(1186) }
{ method(984) reconstruct(947) comput(926) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(1367) use(1326) method(1137) }
{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
{ signal(2180) analysi(812) frequenc(800) }
{ patient(1821) servic(1111) care(1106) }
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{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
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{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ 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) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier.MATERIALS AND METHODS: The system combines rule-based and machine learning approaches that rely on morphological, lexical, syntactic, semantic, and domain-specific features. Rule-based components were designed to handle the recognition and normalization of temporal expressions, while conditional random fields models were trained for event and temporal recognition.RESULTS: The system achieved micro F scores of 90% for the extraction of temporal expressions and 87% for clinical event extraction. The normalization component for temporal expressions achieved accuracies of 84.73% (expression's type), 70.44% (value), and 82.75% (modifier).DISCUSSION: Compared to the initial agreement between human annotators (87-89%), the system provided comparable performance for both event and temporal expression mining. While (lenient) identification of such mentions is achievable, finding the exact boundaries proved challenging.CONCLUSIONS: The system provides a state-of-the-art method that can be used to support automated identification of mentions of clinical events and temporal expressions in narratives either to support the manual review process or as a part of a large-scale processing of electronic health databases.

Resumo Limpo

jectiv identif clinic event eg problem test treatment associ tempor express eg date time key task extract manag data electron health record part ib natur languag process clinic data challeng develop evalu system automat extract tempor express event clinic narrat extract tempor express addit normal assign type valu modifiermateri method system combin rulebas machin learn approach reli morpholog lexic syntact semant domainspecif featur rulebas compon design handl recognit normal tempor express condit random field model train event tempor recognitionresult system achiev micro f score extract tempor express clinic event extract normal compon tempor express achiev accuraci express type valu modifierdiscuss compar initi agreement human annot system provid compar perform event tempor express mine lenient identif mention achiev find exact boundari prove challengingconclus system provid stateoftheart method can use support autom identif mention clinic event tempor express narrat either support manual review process part largescal process electron health databas

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