Comput. Biol. Med. - Density-based Monte Carlo filter and its applications in nonlinear stochastic differential equation models.

Tópicos

{ model(3480) simul(1196) paramet(876) }
{ estim(2440) model(1874) function(577) }
{ algorithm(1844) comput(1787) effici(935) }
{ state(1844) use(1261) util(961) }
{ problem(2511) optim(1539) algorithm(950) }
{ imag(1057) registr(996) error(939) }
{ network(2748) neural(1063) input(814) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ model(2220) cell(1177) simul(1124) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
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{ search(2224) databas(1162) retriev(909) }
{ perform(999) metric(946) measur(919) }
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{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
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{ sampl(1606) size(1419) use(1276) }
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{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ 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) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ 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

Nonlinear stochastic differential equation models with unobservable state variables are now widely used in analysis of PK/PD data. Unobservable state variables are usually estimated with extended Kalman filter (EKF), and the unknown pharmacokinetic parameters are usually estimated by maximum likelihood estimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE is known to be biased downwards. A density-based Monte Carlo filter (DMF) is proposed to estimate the unobservable state variables, and a simulation-based M estimator is proposed to estimate the unknown parameters in this paper, where a genetic algorithm is designed to search the optimal values of pharmacokinetic parameters. The performances of EKF and DMF are compared through simulations for discrete time and continuous time systems respectively, and it is found that the results based on DMF are more accurate than those given by EKF with respect to mean absolute error.

Resumo Limpo

nonlinear stochast differenti equat model unobserv state variabl now wide use analysi pkpd data unobserv state variabl usual estim extend kalman filter ekf unknown pharmacokinet paramet usual estim maximum likelihood estim howev ekf inadequ nonlinear pkpd model mle known bias downward densitybas mont carlo filter dmf propos estim unobserv state variabl simulationbas m estim propos estim unknown paramet paper genet algorithm design search optim valu pharmacokinet paramet perform ekf dmf compar simul discret time continu time system respect found result base dmf accur given ekf respect mean absolut error

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