AMIA Annu Symp Proc - Modeling patient safety incidents knowledge with the Categorial Structure method.

Tópicos

{ concept(1167) ontolog(924) domain(897) }
{ framework(1458) process(801) describ(734) }
{ treatment(1704) effect(941) patient(846) }
{ medic(1828) order(1363) alert(1069) }
{ structur(1116) can(940) graph(676) }
{ inform(2794) health(2639) internet(1427) }
{ import(1318) role(1303) understand(862) }
{ research(1218) medic(880) student(794) }
{ use(976) code(926) identifi(902) }
{ decis(3086) make(1611) patient(1517) }
{ imag(1947) propos(1133) code(1026) }
{ sequenc(1873) structur(1644) protein(1328) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ chang(1828) time(1643) increas(1301) }
{ clinic(1479) use(1117) guidelin(835) }
{ search(2224) databas(1162) retriev(909) }
{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
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{ health(3367) inform(1360) care(1135) }
{ data(2317) use(1299) case(1017) }
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{ survey(1388) particip(1329) question(1065) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ 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) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ 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(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Following the WHO initiative named World Alliance for Patient Safety (PS) launched in 2004 a conceptual framework developed by PS national reporting experts has summarized the knowledge available. As a second step, the Department of Public Health of the University of Saint Etienne team elaborated a Categorial Structure (a semi formal structure not related to an upper level ontology) identifying the elements of the semantic structure underpinning the broad concepts contained in the framework for patient safety. This knowledge engineering method has been developed to enable modeling patient safety information as a prerequisite for subsequent full ontology development. The present article describes the semantic dissection of the concepts, the elicitation of the ontology requirements and the domain constraints of the conceptual framework. This ontology includes 134 concepts and 25 distinct relations and will serve as basis for an Information Model for Patient Safety.

Resumo Limpo

follow initi name world allianc patient safeti ps launch conceptu framework develop ps nation report expert summar knowledg avail second step depart public health univers saint etienn team elabor categori structur semi formal structur relat upper level ontolog identifi element semant structur underpin broad concept contain framework patient safeti knowledg engin method develop enabl model patient safeti inform prerequisit subsequ full ontolog develop present articl describ semant dissect concept elicit ontolog requir domain constraint conceptu framework ontolog includ concept distinct relat will serv basi inform model patient safeti

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