AMIA Annu Symp Proc - A Knowledge Intensive Approach to Mapping Clinical Narrative to LOINC.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ case(1353) use(1143) diagnosi(1136) }
{ concept(1167) ontolog(924) domain(897) }
{ data(2317) use(1299) case(1017) }
{ analysi(2126) use(1163) compon(1037) }
{ can(774) often(719) complex(702) }
{ inform(2794) health(2639) internet(1427) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ blood(1257) pressur(1144) flow(957) }
{ monitor(1329) mobil(1314) devic(1160) }
{ activ(1138) subject(705) human(624) }
{ use(2086) technolog(871) perceiv(783) }
{ use(976) code(926) identifi(902) }
{ method(1219) similar(1157) match(930) }
{ network(2748) neural(1063) input(814) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ method(1557) propos(1049) approach(1037) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ perform(999) metric(946) measur(919) }
{ state(1844) use(1261) util(961) }
{ first(2504) two(1366) second(1323) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }
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{ data(1737) use(1416) pattern(1282) }
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{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
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{ learn(2355) train(1041) set(1003) }
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{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
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{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ 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) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ 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(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Many natural language processing systems are being applied to clinical text, yet clinically useful results are obtained only by honing a system to a particular context. We suggest that concentration on the information needed for this processing is crucial and present a knowledge intensive methodology for mapping clinical text to LOINC. The system takes published case reports as input and maps vital signs and body measurements and reports of diagnostic procedures to fully specified LOINC codes. Three kinds of knowledge are exploited: textual, ontological, and pragmatic (including information about physiology and the clinical process). Evaluation on 4809 sentences yielded precision of 89% and recall of 93% (F-score 0.91). Our method could form the basis for a system to provide semi-automated help to human coders.

Resumo Limpo

mani natur languag process system appli clinic text yet clinic use result obtain hone system particular context suggest concentr inform need process crucial present knowledg intens methodolog map clinic text loinc system take publish case report input map vital sign bodi measur report diagnost procedur fulli specifi loinc code three kind knowledg exploit textual ontolog pragmat includ inform physiolog clinic process evalu sentenc yield precis recal fscore method form basi system provid semiautom help human coder

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