AMIA Annu Symp Proc - Toward a two-tier clinical warning system for hospitalized patients.

Tópicos

{ monitor(1329) mobil(1314) devic(1160) }
{ record(1888) medic(1808) patient(1693) }
{ detect(2391) sensit(1101) algorithm(908) }
{ time(1939) patient(1703) rate(768) }
{ method(2212) result(1239) propos(1039) }
{ framework(1458) process(801) describ(734) }
{ analysi(2126) use(1163) compon(1037) }
{ assess(1506) score(1403) qualiti(1306) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(1557) propos(1049) approach(1037) }
{ risk(3053) factor(974) diseas(938) }
{ model(2341) predict(2261) use(1141) }
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{ perform(1367) use(1326) method(1137) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ 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) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, we envision a two-tiered early warning system designed to identify the signs of clinical deterioration and provide early warning of serious clinical events. The first tier of the system automatically identifies patients at risk of clinical deterioration from existing electronic medical record databases. The second tier performs real-time clinical event detection based on real-time vital sign data collected from on-body wireless sensors attached to those high-risk patients. We employ machine-learning techniques to analyze data from both tiers, assigning scores to patients in real time. The assigned scores can then be used to trigger early-intervention alerts. Preliminary study of an early warning system component and a wireless clinical monitoring system component demonstrate the feasibility of this two-tiered approach.

Resumo Limpo

clinic studi found earli detect intervent essenti prevent clinic deterior patient general hospit unit paper envis twotier earli warn system design identifi sign clinic deterior provid earli warn serious clinic event first tier system automat identifi patient risk clinic deterior exist electron medic record databas second tier perform realtim clinic event detect base realtim vital sign data collect onbodi wireless sensor attach highrisk patient employ machinelearn techniqu analyz data tier assign score patient real time assign score can use trigger earlyintervent alert preliminari studi earli warn system compon wireless clinic monitor system compon demonstr feasibl twotier approach

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