AMIA Annu Symp Proc - Identifying Granularity Differences between Large Biomedical Ontologies through Rules.

Tópicos

{ concept(1167) ontolog(924) domain(897) }
{ system(1976) rule(880) can(841) }
{ featur(3375) classif(2383) classifi(1994) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1219) similar(1157) match(930) }
{ perform(999) metric(946) measur(919) }
{ extract(1171) text(1153) clinic(932) }
{ state(1844) use(1261) util(961) }
{ first(2504) two(1366) second(1323) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ framework(1458) process(801) describ(734) }
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{ studi(1410) differ(1259) use(1210) }
{ perform(1367) use(1326) method(1137) }
{ model(2656) set(1616) predict(1553) }
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{ data(3008) multipl(1320) sourc(1022) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
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{ method(1969) cluster(1462) data(1082) }
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{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
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{ bind(1733) structur(1185) ligand(1036) }
{ imag(2830) propos(1344) filter(1198) }
{ 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) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ 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) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ 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) }
{ 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) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The objective of this study is to identify the granularity differences as well as similarity between large biomedical ontologies through rules. Two anatomical ontologies were selected, and based on a set of concept mappings obtained through simple string matching techniques, we constructed rules to distinguish among different types of subclasses and classifications. 82% of the concept mappings have exactly the same classification in subclasses between the two ontologies. Other mappings are classified in different granularity, including additional subclasses, detailed classification, and different intermediate classification concepts. Using rules and the rule inference engine enables an automatic and scalable investigation of the structural incompatibility among biomedical ontologies.

Resumo Limpo

object studi identifi granular differ well similar larg biomed ontolog rule two anatom ontolog select base set concept map obtain simpl string match techniqu construct rule distinguish among differ type subclass classif concept map exact classif subclass two ontolog map classifi differ granular includ addit subclass detail classif differ intermedi classif concept use rule rule infer engin enabl automat scalabl investig structur incompat among biomed ontolog

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