AMIA Annu Symp Proc - Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes.

Tópicos

{ patient(2315) diseas(1263) diabet(1191) }
{ data(3963) clinic(1234) research(1004) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ studi(2440) review(1878) systemat(933) }
{ risk(3053) factor(974) diseas(938) }
{ studi(1119) effect(1106) posit(819) }
{ imag(1947) propos(1133) code(1026) }
{ method(1219) similar(1157) match(930) }
{ problem(2511) optim(1539) algorithm(950) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ featur(1941) imag(1645) propos(1176) }
{ cost(1906) reduc(1198) effect(832) }
{ cancer(2502) breast(956) screen(824) }
{ detect(2391) sensit(1101) algorithm(908) }
{ inform(2794) health(2639) internet(1427) }
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{ framework(1458) process(801) describ(734) }
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{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
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{ imag(1057) registr(996) error(939) }
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{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
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{ concept(1167) ontolog(924) domain(897) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ 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) }
{ patient(1821) servic(1111) care(1106) }
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{ 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) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

For many clinical conditions, only a small number of patients experience adverse outcomes. Developing risk stratification algorithms for these conditions typically requires collecting large volumes of data to capture enough positive and negative for training. This process is slow, expensive, and may not be appropriate for new phenomena. In this paper, we explore different anomaly detection approaches to identify high-risk patients as cases that lie in sparse regions of the feature space. We study three broad categories of anomaly detection methods: classification-based, nearest neighbor-based, and clustering-based techniques. When evaluated on data from the National Surgical Quality Improvement Program (NSQIP), these methods were able to successfully identify patients at an elevated risk of mortality and rare morbidities following inpatient surgical procedures.

Resumo Limpo

mani clinic condit small number patient experi advers outcom develop risk stratif algorithm condit typic requir collect larg volum data captur enough posit negat train process slow expens may appropri new phenomena paper explor differ anomali detect approach identifi highrisk patient case lie spars region featur space studi three broad categori anomali detect method classificationbas nearest neighborbas clusteringbas techniqu evalu data nation surgic qualiti improv program nsqip method abl success identifi patient elev risk mortal rare morbid follow inpati surgic procedur

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