J Biomed Inform - Degree centrality for semantic abstraction summarization of therapeutic studies.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ structur(1116) can(940) graph(676) }
{ research(1085) discuss(1038) issu(1018) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ assess(1506) score(1403) qualiti(1306) }
{ record(1888) medic(1808) patient(1693) }
{ treatment(1704) effect(941) patient(846) }
{ chang(1828) time(1643) increas(1301) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ sampl(1606) size(1419) use(1276) }
{ high(1669) rate(1365) level(1280) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ featur(1941) imag(1645) propos(1176) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ drug(1928) target(777) effect(648) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ 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) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ 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) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ 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) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ 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) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Automatic summarization has been proposed to help manage the results of biomedical information retrieval systems. Semantic MEDLINE, for example, summarizes semantic predications representing assertions in MEDLINE citations. Results are presented as a graph which maintains links to the original citations. Graphs summarizing more than 500 citations are hard to read and navigate, however. We exploit graph theory for focusing these large graphs. The method is based on degree centrality, which measures connectedness in a graph. Four categories of clinical concepts related to treatment of disease were identified and presented as a summary of input text. A baseline was created using term frequency of occurrence. The system was evaluated on summaries for treatment of five diseases compared to a reference standard produced manually by two physicians. The results showed that recall for system results was 72%, precision was 73%, and F-score was 0.72. The system F-score was considerably higher than that for the baseline (0.47).

Resumo Limpo

automat summar propos help manag result biomed inform retriev system semant medlin exampl summar semant predic repres assert medlin citat result present graph maintain link origin citat graph summar citat hard read navig howev exploit graph theori focus larg graph method base degre central measur connected graph four categori clinic concept relat treatment diseas identifi present summari input text baselin creat use term frequenc occurr system evalu summari treatment five diseas compar refer standard produc manual two physician result show recal system result precis fscore system fscore consider higher baselin

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