AMIA Annu Symp Proc - Cohort amplification: an associative classification framework for identification of disease cohorts in the electronic health record.

Tópicos

{ data(2317) use(1299) case(1017) }
{ clinic(1479) use(1117) guidelin(835) }
{ ehr(2073) health(1662) electron(1139) }
{ data(1737) use(1416) pattern(1282) }
{ system(1976) rule(880) can(841) }
{ gene(2352) biolog(1181) express(1162) }
{ patient(2315) diseas(1263) diabet(1191) }
{ framework(1458) process(801) describ(734) }
{ extract(1171) text(1153) clinic(932) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ featur(3375) classif(2383) classifi(1994) }
{ concept(1167) ontolog(924) domain(897) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ patient(1821) servic(1111) care(1106) }
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{ patient(2837) hospit(1953) medic(668) }
{ 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) }
{ 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) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ 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) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

With the growing national dissemination of the electronic health record (EHR), there are expectations that algorithms to identify disease-based cohorts for health services research will be deployable across health care organizations. Toward that goal, a novel associative classification framework was designed to generate prediction rules to identify cases similar to the exemplar cases on which it was trained. It processes exemplars for any medical condition without modification. The framework is distinguished by core candidate data attributes based on common EHR observation categories, application of associative classification methods to cull disease-specific attributes and predictive rules from the core attributes, and support for attribute concept hierarchies to manage the various layers of granularity in native EHR data. The framework processes and an evaluation of prediction rules generated to identify diabetes mellitus are presented.

Resumo Limpo

grow nation dissemin electron health record ehr expect algorithm identifi diseasebas cohort health servic research will deploy across health care organ toward goal novel associ classif framework design generat predict rule identifi case similar exemplar case train process exemplar medic condit without modif framework distinguish core candid data attribut base common ehr observ categori applic associ classif method cull diseasespecif attribut predict rule core attribut support attribut concept hierarchi manag various layer granular nativ ehr data framework process evalu predict rule generat identifi diabet mellitus present

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