Methods Inf Med - Prediction model for glucose metabolism based on lipid metabolism.

Tópicos

{ model(3480) simul(1196) paramet(876) }
{ patient(2315) diseas(1263) diabet(1191) }
{ model(2656) set(1616) predict(1553) }
{ error(1145) method(1030) estim(1020) }
{ state(1844) use(1261) util(961) }
{ drug(1928) target(777) effect(648) }
{ measur(2081) correl(1212) valu(896) }
{ assess(1506) score(1403) qualiti(1306) }
{ clinic(1479) use(1117) guidelin(835) }
{ time(1939) patient(1703) rate(768) }
{ blood(1257) pressur(1144) flow(957) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(1737) use(1416) pattern(1282) }
{ imag(2675) segment(2577) method(1081) }
{ risk(3053) factor(974) diseas(938) }
{ spatial(1525) area(1432) region(1030) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ imag(2830) propos(1344) filter(1198) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ perform(1367) use(1326) method(1137) }
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{ implement(1333) system(1263) develop(1122) }
{ method(2212) result(1239) propos(1039) }
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{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ take(945) account(800) differ(722) }
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{ visual(1396) interact(850) tool(830) }
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{ studi(1119) effect(1106) posit(819) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
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{ health(1844) social(1437) communiti(874) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVES: We developed a robust, long-term clinical prediction model to predict conditions leading to early diabetes using laboratory values other than blood glucose and insulin levels. Our model protects against missing data and noise that occur during long-term analysis.METHODS: RESULTS of a 75-g oral glucose tolerance test (OGTT) were divided into three groups: diabetes, impaired glucose tolerance (IGT), and normal (n = 114, 235, and 325, respectively). For glucose metabolic and lipid metabolic parameters, near 30-day mean values and 10-year integrated values were compared. The relation between high-density lipoprotein cholesterol (HDL-C) and variations in HbA1c was analyzed in 158 patients. We also constructed a state space model consisting of an observation model (HDL-C and HbA1c) and an internal model (disorders of lipid metabolism and glucose metabolism) and applied this model to 116 cases.RESULTS: The root mean square error between the observed HbA1c and predicted HbA1c was 0.25.CONCLUSIONS: In the observation model, HDL-C levels were useful for prediction of increases in HbA1c. Even with numerous missing values over time, as occurs in clinical practice, clinically valid predictions can be made using this state space model.

Resumo Limpo

jectiv develop robust longterm clinic predict model predict condit lead earli diabet use laboratori valu blood glucos insulin level model protect miss data nois occur longterm analysismethod result g oral glucos toler test ogtt divid three group diabet impair glucos toler igt normal n respect glucos metabol lipid metabol paramet near day mean valu year integr valu compar relat highdens lipoprotein cholesterol hdlc variat hbac analyz patient also construct state space model consist observ model hdlc hbac intern model disord lipid metabol glucos metabol appli model casesresult root mean squar error observ hbac predict hbac conclus observ model hdlc level use predict increas hbac even numer miss valu time occur clinic practic clinic valid predict can made use state space model

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