AMIA Annu Symp Proc - Semantic annotation of clinical events for generating a problem list.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ data(1737) use(1416) pattern(1282) }
{ use(976) code(926) identifi(902) }
{ assess(1506) score(1403) qualiti(1306) }
{ error(1145) method(1030) estim(1020) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1119) effect(1106) posit(819) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ medic(1828) order(1363) alert(1069) }
{ activ(1138) subject(705) human(624) }
{ method(2212) result(1239) propos(1039) }
{ system(1976) rule(880) can(841) }
{ data(3963) clinic(1234) research(1004) }
{ model(2341) predict(2261) use(1141) }
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{ case(1353) use(1143) diagnosi(1136) }
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{ risk(3053) factor(974) diseas(938) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ 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) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We present a pilot study of an annotation schema representing problems and their attributes, along with their relationship to temporal modifiers. We evaluated the ability for humans to annotate clinical reports using the schema and assessed the contribution of semantic annotations in determining the status of a problem mention as active, inactive, proposed, resolved, negated, or other. Our hypothesis is that the schema captures semantic information useful for generating an accurate problem list. Clinical named entities such as reference events, time points, time durations, aspectual phase, ordering words and their relationships including modifications and ordering relations can be annotated by humans with low to moderate recall. Once identified, most attributes can be annotated with low to moderate agreement. Some attributes - Experiencer, Existence, and Certainty - are more informative than other attributes - Intermittency and Generalized/Conditional - for predicting a problem mention's status. Support vector machine outperformed Na?ve Bayes and Decision Tree for predicting a problem's status.

Resumo Limpo

present pilot studi annot schema repres problem attribut along relationship tempor modifi evalu abil human annot clinic report use schema assess contribut semant annot determin status problem mention activ inact propos resolv negat hypothesi schema captur semant inform use generat accur problem list clinic name entiti refer event time point time durat aspectu phase order word relationship includ modif order relat can annot human low moder recal identifi attribut can annot low moder agreement attribut experienc exist certainti inform attribut intermitt generalizedcondit predict problem mention status support vector machin outperform nave bay decis tree predict problem status

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