J. Med. Internet Res. - Cumulative query method for influenza surveillance using search engine data.

Tópicos

{ search(2224) databas(1162) retriev(909) }
{ method(1219) similar(1157) match(930) }
{ method(2212) result(1239) propos(1039) }
{ data(2317) use(1299) case(1017) }
{ measur(2081) correl(1212) valu(896) }
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{ age(1611) year(1155) adult(843) }
{ concept(1167) ontolog(924) domain(897) }
{ model(2656) set(1616) predict(1553) }
{ assess(1506) score(1403) qualiti(1306) }
{ risk(3053) factor(974) diseas(938) }
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{ time(1939) patient(1703) rate(768) }
{ system(1976) rule(880) can(841) }
{ motion(1329) object(1292) video(1091) }
{ howev(809) still(633) remain(590) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ high(1669) rate(1365) level(1280) }
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{ algorithm(1844) comput(1787) effici(935) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ use(2086) technolog(871) perceiv(783) }
{ structur(1116) can(940) graph(676) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
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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) }
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{ featur(1941) imag(1645) propos(1176) }
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{ studi(1410) differ(1259) use(1210) }
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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ 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(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
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{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea.OBJECTIVES: The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data.METHODS: Our study was based on the local search engine, Daum (approximately 25% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson's correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient.RESULTS: In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of =.7, but 4 of 13 combined queries had an r value of =.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of =.7, but 6 of 15 combined queries had an r value of =.7.CONCLUSIONS: Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set.

Resumo Limpo

ckground internet search queri becom import data sourc syndrom surveil system howev current syndrom surveil system use internet search queri data south koreaobject object studi examin correl cumul queri method nation influenza surveil datamethod studi base local search engin daum approxim market share influenzalik ill ili data korea center diseas control prevent quota sampl survey conduct particip obtain popular queri divid studi period two set set epidemiolog year develop set valid set set develop set valid set pearson correl coeffici calcul daum data ili data develop set select combin queri correl coeffici higher list descend order creat cumul queri method n repres number cumul combin queri descend order correl coefficientresult valid set cumul queri method appli higher correl coeffici min max highest singl combin queri cumul queri method r valu combin queri r valu valid set cumul queri method show higher correl coeffici min max highest singl combin queri cumul queri method r valu combin queri r valu conclus cumul queri method show relat higher correl nation influenza surveil data combin queri develop valid set

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