AMIA Annu Symp Proc - Sample-efficient learning with auxiliary class-label information.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ system(1976) rule(880) can(841) }
{ framework(1458) process(801) describ(734) }
{ method(1557) propos(1049) approach(1037) }
{ system(1050) medic(1026) inform(1018) }
{ perform(999) metric(946) measur(919) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ record(1888) medic(1808) patient(1693) }
{ cost(1906) reduc(1198) effect(832) }
{ data(1737) use(1416) pattern(1282) }
{ network(2748) neural(1063) input(814) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ ehr(2073) health(1662) electron(1139) }
{ medic(1828) order(1363) alert(1069) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ measur(2081) correl(1212) valu(896) }
{ studi(2440) review(1878) systemat(933) }
{ chang(1828) time(1643) increas(1301) }
{ extract(1171) text(1153) clinic(932) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ data(3963) clinic(1234) research(1004) }
{ blood(1257) pressur(1144) flow(957) }
{ patient(2837) hospit(1953) medic(668) }
{ group(2977) signific(1463) compar(1072) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ survey(1388) particip(1329) question(1065) }
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{ sequenc(1873) structur(1644) protein(1328) }
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{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
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{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ 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) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Building classification models from clinical data collected for past patients often requires additional example labeling and annotation by a human expert. Since example labeling may require to review a complete electronic health record the process can be very time consuming and costly. To make the process more cost-efficient, the number of examples an expert needs to label should be reduced. We develop and test a new approach for the classification learning in which, in addition to class labels provided by an expert, the learner is provided with auxiliary information that reflects how strong the expert feels about the class label. We show that this information can be extremely useful for practical classification tasks based on human assessment and can lead to improved learning with a smaller number of examples. We develop a new classification approach based on the support vector machines and the learning to rank methodologies capable of utilizing the auxiliary information during the model learning process. We demonstrate the benefit of the approach on the problem of learning an alert model for Heparin Induced Thrombocytopenia (HIT) by showing an improved classification performance of the models that are trained on a smaller number of labeled examples.

Resumo Limpo

build classif model clinic data collect past patient often requir addit exampl label annot human expert sinc exampl label may requir review complet electron health record process can time consum cost make process costeffici number exampl expert need label reduc develop test new approach classif learn addit class label provid expert learner provid auxiliari inform reflect strong expert feel class label show inform can extrem use practic classif task base human assess can lead improv learn smaller number exampl develop new classif approach base support vector machin learn rank methodolog capabl util auxiliari inform model learn process demonstr benefit approach problem learn alert model heparin induc thrombocytopenia hit show improv classif perform model train smaller number label exampl

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