AMIA Annu Symp Proc - Aligning Structured and Unstructured Medical Problems Using UMLS.

Tópicos

{ record(1888) medic(1808) patient(1693) }
{ imag(1057) registr(996) error(939) }
{ ehr(2073) health(1662) electron(1139) }
{ data(2317) use(1299) case(1017) }
{ imag(2675) segment(2577) method(1081) }
{ problem(2511) optim(1539) algorithm(950) }
{ patient(1821) servic(1111) care(1106) }
{ bind(1733) structur(1185) ligand(1036) }
{ patient(2837) hospit(1953) medic(668) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ use(1733) differ(960) four(931) }
{ imag(2830) propos(1344) filter(1198) }
{ studi(2440) review(1878) systemat(933) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ framework(1458) process(801) describ(734) }
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{ chang(1828) time(1643) increas(1301) }
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{ concept(1167) ontolog(924) domain(897) }
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{ algorithm(1844) comput(1787) effici(935) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ health(3367) inform(1360) care(1135) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper reports a pilot study to align medical problems in structured and unstructured EHR data using UMLS. A total of 120 medical problems in discharge summaries were extracted using NLP software (MedLEE) and aligned with 87 ICD-9 diagnoses for 19 non-overlapping hospital visits of 4 patients. The alignment accuracy was evaluated by a medical doctor. The average overlap of medical problems between the two data sources obtained by our automatic alignment method was 23.8%, which was about half of the manual review result, 43.56%. We discuss the implications for related research in integrating structured and unstructured EHR data.

Resumo Limpo

paper report pilot studi align medic problem structur unstructur ehr data use uml total medic problem discharg summari extract use nlp softwar medle align icd diagnos nonoverlap hospit visit patient align accuraci evalu medic doctor averag overlap medic problem two data sourc obtain automat align method half manual review result discuss implic relat research integr structur unstructur ehr data

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