Appl Clin Inform - Ontology content patterns as bridge for the semantic representation of clinical information.

Tópicos

{ concept(1167) ontolog(924) domain(897) }
{ record(1888) medic(1808) patient(1693) }
{ activ(1452) weight(1219) physic(1104) }
{ inform(2794) health(2639) internet(1427) }
{ data(3963) clinic(1234) research(1004) }
{ signal(2180) analysi(812) frequenc(800) }
{ estim(2440) model(1874) function(577) }
{ framework(1458) process(801) describ(734) }
{ data(1714) softwar(1251) tool(1186) }
{ featur(1941) imag(1645) propos(1176) }
{ research(1085) discuss(1038) issu(1018) }
{ state(1844) use(1261) util(961) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ use(1733) differ(960) four(931) }
{ data(1737) use(1416) pattern(1282) }
{ method(1219) similar(1157) match(930) }
{ error(1145) method(1030) estim(1020) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ model(2656) set(1616) predict(1553) }
{ use(2086) technolog(871) perceiv(783) }
{ drug(1928) target(777) effect(648) }
{ decis(3086) make(1611) patient(1517) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
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{ system(1976) rule(880) can(841) }
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{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ 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) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ 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) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ 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) }
{ 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) }
{ first(2504) two(1366) second(1323) }
{ 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) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: Semantic interoperability of the Electronic Health Record (EHR) requires a rigorous and precise modelling of clinical information. Our objective is to facilitate the representation of clinical facts based on formal principles.METHODS: We here explore the potential of ontology content patterns, which are grounded on a formal and semantically rich ontology model and can be specialised and composed.RESULTS: We describe and apply two content patterns for the representation of data on tobacco use, rendered according to two heterogeneous models, represented in openEHR and in HL7 CDA. Finally, we provide some query exemplars that demonstrate a data interoperability use case.CONCLUSION: The use of ontology content patterns facilitate the semantic representation of clinical information and therefore improve their semantic interoperability. There are open issues such as the scalability and performance of the approach if a logic-based language is used. Implementation decisions might determine the final degree of semantic interoperability, influenced by the state of the art of the semantic technologies.

Resumo Limpo

jectiv semant interoper electron health record ehr requir rigor precis model clinic inform object facilit represent clinic fact base formal principlesmethod explor potenti ontolog content pattern ground formal semant rich ontolog model can specialis composedresult describ appli two content pattern represent data tobacco use render accord two heterogen model repres openehr hl cda final provid queri exemplar demonstr data interoper use caseconclus use ontolog content pattern facilit semant represent clinic inform therefor improv semant interoper open issu scalabl perform approach logicbas languag use implement decis might determin final degre semant interoper influenc state art semant technolog

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