AMIA Annu Symp Proc - Generalizability and comparison of automatic clinical text de-identification methods and resources.

Tópicos

{ extract(1171) text(1153) clinic(932) }
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{ take(945) account(800) differ(722) }
{ imag(1057) registr(996) error(939) }
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{ measur(2081) correl(1212) valu(896) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ case(1353) use(1143) diagnosi(1136) }
{ ehr(2073) health(1662) electron(1139) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ model(3404) distribut(989) bayesian(671) }
{ system(1976) rule(880) can(841) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ 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) }
{ 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) }
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{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
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{ cancer(2502) breast(956) screen(824) }
{ 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) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

In this paper, we present an evaluation of the hybrid best-of-breed automated VHA (Veteran's Health Administration) clinical text de-identification system, nicknamed BoB, developed within the VHA Consortium for Healthcare Informatics Research. We also evaluate two available machine learning-based text de-identifications systems: MIST and HIDE. Two different clinical corpora were used for this evaluation: a manually annotated VHA corpus, and the 2006 i2b2 de-identification challenge corpus. These experiments focus on the generalizability and portability of the classification models across different document sources. BoB demonstrated good recall (92.6%), satisfactorily prioritizing patient privacy, and also achieved competitive precision (83.6%) for preserving subsequent document interpretability. MIST and HIDE reached very competitive results, in most cases with high precision (92.6% and 93.6%), although recall was sometimes lower than desired for the most sensitive PHI categories.

Resumo Limpo

paper present evalu hybrid bestofbre autom vha veteran health administr clinic text deidentif system nicknam bob develop within vha consortium healthcar informat research also evalu two avail machin learningbas text deidentif system mist hide two differ clinic corpora use evalu manual annot vha corpus ib deidentif challeng corpus experi focus generaliz portabl classif model across differ document sourc bob demonstr good recal satisfactorili priorit patient privaci also achiev competit precis preserv subsequ document interpret mist hide reach competit result case high precis although recal sometim lower desir sensit phi categori

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