AMIA Annu Symp Proc - Does query expansion limit our learning? A comparison of social-based expansion to content-based expansion for medical queries on the internet.

Tópicos

{ search(2224) databas(1162) retriev(909) }
{ concept(1167) ontolog(924) domain(897) }
{ group(2977) signific(1463) compar(1072) }
{ result(1111) use(1088) new(759) }
{ visual(1396) interact(850) tool(830) }
{ first(2504) two(1366) second(1323) }
{ health(1844) social(1437) communiti(874) }
{ method(2212) result(1239) propos(1039) }
{ can(774) often(719) complex(702) }
{ imag(1057) registr(996) error(939) }
{ data(3963) clinic(1234) research(1004) }
{ perform(999) metric(946) measur(919) }
{ studi(1119) effect(1106) posit(819) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ use(1733) differ(960) four(931) }
{ data(1737) use(1416) pattern(1282) }
{ sequenc(1873) structur(1644) protein(1328) }
{ assess(1506) score(1403) qualiti(1306) }
{ error(1145) method(1030) estim(1020) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ compound(1573) activ(1297) structur(1058) }
{ age(1611) year(1155) adult(843) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ 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) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
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{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ 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) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ 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) }
{ perform(1367) use(1326) method(1137) }
{ 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) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Searching for medical information online is a common activity. While it has been shown that forming good queries is difficult, Google's query suggestion tool, a type of query expansion, aims to facilitate query formation. However, it is unknown how this expansion, which is based on what others searched for, affects the information gathering of the online community. To measure the impact of social-based query expansion, this study compared it with content-based expansion, i.e., what is really in the text. We used 138,906 medical queries from the AOL User Session Collection and expanded them using Google's Autocomplete method (social-based) and the content of the Google Web Corpus (content-based). We evaluated the specificity and ambiguity of the expansion terms for trigram queries. We also looked at the impact on the actual results using domain diversity and expansion edit distance. Results showed that the social-based method provided more precise expansion terms as well as terms that were less ambiguous. Expanded queries do not differ significantly in diversity when expanded using the social-based method (6.72 different domains returned in the first ten results, on average) vs. content-based method (6.73 different domains, on average).

Resumo Limpo

search medic inform onlin common activ shown form good queri difficult googl queri suggest tool type queri expans aim facilit queri format howev unknown expans base other search affect inform gather onlin communiti measur impact socialbas queri expans studi compar contentbas expans ie realli text use medic queri aol user session collect expand use googl autocomplet method socialbas content googl web corpus contentbas evalu specif ambigu expans term trigram queri also look impact actual result use domain divers expans edit distanc result show socialbas method provid precis expans term well term less ambigu expand queri differ signific divers expand use socialbas method differ domain return first ten result averag vs contentbas method differ domain averag

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