J Med Syst - Application of data mining on the development of a disease distribution map of screened community residents of Taipei county in Taiwan.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ age(1611) year(1155) adult(843) }
{ patient(2315) diseas(1263) diabet(1191) }
{ method(1219) similar(1157) match(930) }
{ problem(2511) optim(1539) algorithm(950) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ analysi(2126) use(1163) compon(1037) }
{ decis(3086) make(1611) patient(1517) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ system(1050) medic(1026) inform(1018) }
{ can(981) present(881) function(850) }
{ data(1737) use(1416) pattern(1282) }
{ sequenc(1873) structur(1644) protein(1328) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ method(984) reconstruct(947) comput(926) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ perform(1367) use(1326) method(1137) }
{ health(3367) inform(1360) care(1135) }
{ research(1218) medic(880) student(794) }
{ activ(1138) subject(705) human(624) }
{ structur(1116) can(940) graph(676) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ 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) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ 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) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ 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) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ 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) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This study used data mining techniques to investigate disease forms in various administrative areas and to analyze the differences among various administrative areas in order to further draw up a disease distribution map. It is hoped that may help formulate future public health strategies and to allocate medical resources more appropriately. The major disease forms for residents under the age of 60 were hypertension, hyperglycemia and hyperlipidemia. In regard to the neighboring areas, three neighboring areas, A1, A3, and B9, shared the same disease problems with A4, A5, and B3, while two mountain-area cities, B7 and C10, experienced higher instances of liver function impairment. In terms of the clustering phenomenon among municipally graded administrative areas, the major health problems in Grade A cities were hypertension, hyperglycemia, and hyperlipidemia. The health problems such as liver function impairment and renal dysfunction were more frequently observed in Grade B and Grade C cities.

Resumo Limpo

studi use data mine techniqu investig diseas form various administr area analyz differ among various administr area order draw diseas distribut map hope may help formul futur public health strategi alloc medic resourc appropri major diseas form resid age hypertens hyperglycemia hyperlipidemia regard neighbor area three neighbor area b share diseas problem b two mountainarea citi b c experienc higher instanc liver function impair term cluster phenomenon among municip grade administr area major health problem grade citi hypertens hyperglycemia hyperlipidemia health problem liver function impair renal dysfunct frequent observ grade b grade c citi

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