AMIA Annu Symp Proc - Decision path models for patient-specific modeling of patient outcomes.

Tópicos

{ model(2341) predict(2261) use(1141) }
{ featur(3375) classif(2383) classifi(1994) }
{ case(1353) use(1143) diagnosi(1136) }
{ structur(1116) can(940) graph(676) }
{ howev(809) still(633) remain(590) }
{ decis(3086) make(1611) patient(1517) }
{ treatment(1704) effect(941) patient(846) }
{ model(3480) simul(1196) paramet(876) }
{ can(981) present(881) function(850) }
{ risk(3053) factor(974) diseas(938) }
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{ care(1570) inform(1187) nurs(1089) }
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{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
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{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ 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) }

Resumo

Patient-specific models are constructed to take advantage of the particular features of the patient case of interest compared to commonly used population-wide models that are constructed to perform well on average on all cases. We introduce two patient-specific algorithms that are based on the decision tree paradigm. These algorithms construct a decision path specific for each patient of interest compared to a single population-wide decision tree with many paths that is applicable to all patients of interest that are constructed by standard algorithms. We applied the patient-specific algorithms to predict five different outcomes in clinical datasets. Compared to the population-wide CART decision tree the patient-specific decision path models had superior performance on area under the ROC curve (AUC) and had comparable performance on balanced accuracy. Our results provide support for patient-specific algorithms being a promising approach for predicting clinical outcomes.

Resumo Limpo

patientspecif model construct take advantag particular featur patient case interest compar common use populationwid model construct perform well averag case introduc two patientspecif algorithm base decis tree paradigm algorithm construct decis path specif patient interest compar singl populationwid decis tree mani path applic patient interest construct standard algorithm appli patientspecif algorithm predict five differ outcom clinic dataset compar populationwid cart decis tree patientspecif decis path model superior perform area roc curv auc compar perform balanc accuraci result provid support patientspecif algorithm promis approach predict clinic outcom

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