Comput. Biol. Med. - Results on mining NHANES data: a case study in evidence-based medicine.

Tópicos

{ age(1611) year(1155) adult(843) }
{ survey(1388) particip(1329) question(1065) }
{ data(1737) use(1416) pattern(1282) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ framework(1458) process(801) describ(734) }
{ method(1557) propos(1049) approach(1037) }
{ search(2224) databas(1162) retriev(909) }
{ blood(1257) pressur(1144) flow(957) }
{ use(2086) technolog(871) perceiv(783) }
{ method(1969) cluster(1462) data(1082) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(2830) propos(1344) filter(1198) }
{ patient(2315) diseas(1263) diabet(1191) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ data(3963) clinic(1234) research(1004) }
{ compound(1573) activ(1297) structur(1058) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ research(1218) medic(880) student(794) }
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{ model(3404) distribut(989) bayesian(671) }
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{ imag(1057) registr(996) error(939) }
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{ sequenc(1873) structur(1644) protein(1328) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ extract(1171) text(1153) clinic(932) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
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{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ 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) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ 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) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The National Health and Nutrition Examination Survey (NHANES), administered annually by the National Center for Health Statistics, is designed to assess the general health and nutritional status of adults and children in the United States. Given to several thousands of individuals, the extent of this survey is very broad, covering demographic, laboratory and examination information, as well as responses to a fairly comprehensive health questionnaire. In this paper, we adapt and extend association rule mining and clustering algorithms to extract useful knowledge regarding diabetes and high blood pressure from the 1999-2008 survey results, thus demonstrating how data mining techniques may be used to support evidence-based medicine.

Resumo Limpo

nation health nutrit examin survey nhane administ annual nation center health statist design assess general health nutrit status adult children unit state given sever thousand individu extent survey broad cover demograph laboratori examin inform well respons fair comprehens health questionnair paper adapt extend associ rule mine cluster algorithm extract use knowledg regard diabet high blood pressur survey result thus demonstr data mine techniqu may use support evidencebas medicin

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