J Biomed Inform - An ontology-based measure to compute semantic similarity in biomedicine.

Tópicos

{ concept(1167) ontolog(924) domain(897) }
{ extract(1171) text(1153) clinic(932) }
{ measur(2081) correl(1212) valu(896) }
{ method(1219) similar(1157) match(930) }
{ method(1969) cluster(1462) data(1082) }
{ state(1844) use(1261) util(961) }
{ search(2224) databas(1162) retriev(909) }
{ perform(999) metric(946) measur(919) }
{ record(1888) medic(1808) patient(1693) }
{ system(1976) rule(880) can(841) }
{ assess(1506) score(1403) qualiti(1306) }
{ data(1714) softwar(1251) tool(1186) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ health(3367) inform(1360) care(1135) }
{ data(3008) multipl(1320) sourc(1022) }
{ detect(2391) sensit(1101) algorithm(908) }
{ imag(1947) propos(1133) code(1026) }
{ imag(1057) registr(996) error(939) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ learn(2355) train(1041) set(1003) }
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{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ general(901) number(790) one(736) }
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{ howev(809) still(633) remain(590) }
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{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
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{ activ(1138) subject(705) human(624) }
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{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
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{ error(1145) method(1030) estim(1020) }
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{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ 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) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ 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) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

Proper understanding of textual data requires the exploitation and integration of unstructured and heterogeneous clinical sources, healthcare records or scientific literature, which are fundamental aspects in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that enables the processing, classification and structuring of textual resources. In the past, several approaches for assessing word similarity by exploiting different knowledge sources (ontologies, thesauri, domain corpora, etc.) have been proposed. Some of these measures have been adapted to the biomedical field by incorporating domain information extracted from clinical data or from medical ontologies (such as MeSH or SNOMED CT). In this paper, these approaches are introduced and analyzed in order to determine their advantages and limitations with respect to the considered knowledge bases. After that, a new measure based on the exploitation of the taxonomical structure of a biomedical ontology is proposed. Using SNOMED CT as the input ontology, the accuracy of our proposal is evaluated and compared against other approaches according to a standard benchmark of manually ranked medical terms. The correlation between the results of the evaluated measures and the human experts' ratings shows that our proposal outperforms most of the previous measures avoiding, at the same time, some of their limitations.

Resumo Limpo

proper understand textual data requir exploit integr unstructur heterogen clinic sourc healthcar record scientif literatur fundament aspect clinic translat research determin semant similar word pair import compon text understand enabl process classif structur textual resourc past sever approach assess word similar exploit differ knowledg sourc ontolog thesauri domain corpora etc propos measur adapt biomed field incorpor domain inform extract clinic data medic ontolog mesh snome ct paper approach introduc analyz order determin advantag limit respect consid knowledg base new measur base exploit taxonom structur biomed ontolog propos use snome ct input ontolog accuraci propos evalu compar approach accord standard benchmark manual rank medic term correl result evalu measur human expert rate show propos outperform previous measur avoid time limit

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