BMC Med Inform Decis Mak - A study on PubMed search tag usage pattern: association rule mining of a full-day PubMed query log.


{ inform(2794) health(2639) internet(1427) }
{ search(2224) databas(1162) retriev(909) }
{ data(1737) use(1416) pattern(1282) }
{ use(1733) differ(960) four(931) }
{ age(1611) year(1155) adult(843) }
{ high(1669) rate(1365) level(1280) }
{ extract(1171) text(1153) clinic(932) }
{ system(1050) medic(1026) inform(1018) }
{ health(3367) inform(1360) care(1135) }
{ data(1714) softwar(1251) tool(1186) }
{ perform(999) metric(946) measur(919) }
{ activ(1138) subject(705) human(624) }
{ health(1844) social(1437) communiti(874) }
{ take(945) account(800) differ(722) }
{ featur(1941) imag(1645) propos(1176) }
{ research(1085) discuss(1038) issu(1018) }
{ sampl(1606) size(1419) use(1276) }
{ time(1939) patient(1703) rate(768) }
{ detect(2391) sensit(1101) algorithm(908) }
{ system(1976) rule(880) can(841) }
{ network(2748) neural(1063) input(814) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ design(1359) user(1324) use(1319) }
{ care(1570) inform(1187) nurs(1089) }
{ compound(1573) activ(1297) structur(1058) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ 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) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ 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) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ 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) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ 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) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }


CKGROUND: The practice of evidence-based medicine requires efficient biomedical literature search such as PubMed/MEDLINE. Retrieval performance relies highly on the efficient use of search field tags. The purpose of this study was to analyze PubMed log data in order to understand the usage pattern of search tags by the end user in PubMed/MEDLINE search.METHODS: A PubMed query log file was obtained from the National Library of Medicine containing anonymous user identification, timestamp, and query text. Inconsistent records were removed from the dataset and the search tags were extracted from the query texts. A total of 2,917,159 queries were selected for this study issued by a total of 613,061 users. The analysis of frequent co-occurrences and usage patterns of the search tags was conducted using an association mining algorithm.RESULTS: The percentage of search tag usage was low (11.38% of the total queries) and only 2.95% of queries contained two or more tags. Three out of four users used no search tag and about two-third of them issued less than four queries. Among the queries containing at least one tagged search term, the average number of search tags was almost half of the number of total search terms. Navigational search tags are more frequently used than informational search tags. While no strong association was observed between informational and navigational tags, six (out of 19) informational tags and six (out of 29) navigational tags showed strong associations in PubMed searches.CONCLUSIONS: The low percentage of search tag usage implies that PubMed/MEDLINE users do not utilize the features of PubMed/MEDLINE widely or they are not aware of such features or solely depend on the high recall focused query translation by the PubMed's Automatic Term Mapping. The users need further education and interactive search application for effective use of the search tags in order to fulfill their biomedical information needs from PubMed/MEDLINE.

Resumo Limpo

ckground practic evidencebas medicin requir effici biomed literatur search pubmedmedlin retriev perform reli high effici use search field tag purpos studi analyz pubm log data order understand usag pattern search tag end user pubmedmedlin searchmethod pubm queri log file obtain nation librari medicin contain anonym user identif timestamp queri text inconsist record remov dataset search tag extract queri text total queri select studi issu total user analysi frequent cooccurr usag pattern search tag conduct use associ mine algorithmresult percentag search tag usag low total queri queri contain two tag three four user use search tag twothird issu less four queri among queri contain least one tag search term averag number search tag almost half number total search term navig search tag frequent use inform search tag strong associ observ inform navig tag six inform tag six navig tag show strong associ pubm searchesconclus low percentag search tag usag impli pubmedmedlin user util featur pubmedmedlin wide awar featur sole depend high recal focus queri translat pubm automat term map user need educ interact search applic effect use search tag order fulfil biomed inform need pubmedmedlin

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