IEEE J Biomed Health Inform - Service-oriented medical system for supporting decisions with missing and imbalanced data.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ medic(1828) order(1363) alert(1069) }
{ data(1737) use(1416) pattern(1282) }
{ problem(2511) optim(1539) algorithm(950) }
{ model(2341) predict(2261) use(1141) }
{ blood(1257) pressur(1144) flow(957) }
{ ehr(2073) health(1662) electron(1139) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(2830) propos(1344) filter(1198) }
{ framework(1458) process(801) describ(734) }
{ care(1570) inform(1187) nurs(1089) }
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{ structur(1116) can(940) graph(676) }
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{ network(2748) neural(1063) input(814) }
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{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
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{ error(1145) method(1030) estim(1020) }
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{ extract(1171) text(1153) clinic(932) }
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{ control(1307) perform(991) simul(935) }
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{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ 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) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ 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) }
{ activ(1138) subject(705) human(624) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ 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) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

In this paper, we propose a service-oriented support decision system (SOSDS) for diagnostic problems that is insensitive to the problems of the imbalanced data and missing values of the attributes, which are widely observed in the medical domain. The system is composed of distributed Web services, which implement machine-learning solutions dedicated to constructing the decision models directly from the datasets impaired by the high percentage of missing values of the attributes and imbalanced class distribution. The issue of the imbalanced data is solved by the application of a cost-sensitive support vector machine and the problem of missing values of attributes is handled by proposing the novel ensemble-based approach that splits the incomplete data space into complete subspaces that are further used to construct base learners. We evaluate the quality of the SOSDS components using three ontological datasets.

Resumo Limpo

paper propos serviceori support decis system sosd diagnost problem insensit problem imbalanc data miss valu attribut wide observ medic domain system compos distribut web servic implement machinelearn solut dedic construct decis model direct dataset impair high percentag miss valu attribut imbalanc class distribut issu imbalanc data solv applic costsensit support vector machin problem miss valu attribut handl propos novel ensemblebas approach split incomplet data space complet subspac use construct base learner evalu qualiti sosd compon use three ontolog dataset

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