Neural Comput - Kernels for longitudinal data with variable sequence length and sampling intervals.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ model(2656) set(1616) predict(1553) }
{ analysi(2126) use(1163) compon(1037) }
{ estim(2440) model(1874) function(577) }
{ method(2212) result(1239) propos(1039) }
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{ system(1976) rule(880) can(841) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
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{ state(1844) use(1261) util(961) }
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{ structur(1116) can(940) graph(676) }
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{ health(3367) inform(1360) care(1135) }
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{ 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) }
{ 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) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ 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) }
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{ survey(1388) particip(1329) question(1065) }
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{ 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

We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.

Resumo Limpo

develop sever kernel method classif longitudin data appli detect cognit declin elder first develop mixedeffect model type hierarch empir bay generat model time seri demonstr util likelihood ratio classifi improv standard regress model classifi develop novel fisher kernel base mixtur mixedeffect model use support vector machin classifi hierarch generat model allow us handl variat sequenc length sampl interv grace also give nonparametr kernel base generat model rather reproduc kernel hilbert space appli method detect cognit declin longitudin clinic data motor neuropsycholog test likelihood ratio classifi base neuropsycholog test perform better classifi base motor behavior discrimin classifi perform better likelihood ratio classifi motor behavior test

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