AMIA Annu Symp Proc - Extracting Concepts Related to Homelessness from the Free Text of VA Electronic Medical Records.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ record(1888) medic(1808) patient(1693) }
{ concept(1167) ontolog(924) domain(897) }
{ search(2224) databas(1162) retriev(909) }
{ data(1737) use(1416) pattern(1282) }
{ studi(2440) review(1878) systemat(933) }
{ error(1145) method(1030) estim(1020) }
{ data(3963) clinic(1234) research(1004) }
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{ use(976) code(926) identifi(902) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
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{ blood(1257) pressur(1144) flow(957) }
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{ health(3367) inform(1360) care(1135) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ 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) }
{ patient(1821) servic(1111) care(1106) }
{ 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) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
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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(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Mining the free text of electronic medical records (EMR) using natural language processing (NLP) is an effective method of extracting information not always captured in administrative data. We sought to determine if concepts related to homelessness, a non-medical condition, were amenable to extraction from the EMR of Veterans Affairs (VA) medical records. As there were no off-the-shelf products, a lexicon of terms related to homelessness was created. A corpus of free text documents from outpatient encounters was reviewed to create the reference standard for NLP training and testing. V3NLP Framework was used to detect instances of lexical terms and was compared to the reference standard. With a positive predictive value of 77% for extracting relevant concepts, this study demonstrates the feasibility of extracting positively asserted concepts related to homelessness from the free text of medical records.

Resumo Limpo

mine free text electron medic record emr use natur languag process nlp effect method extract inform alway captur administr data sought determin concept relat homeless nonmed condit amen extract emr veteran affair va medic record offtheshelf product lexicon term relat homeless creat corpus free text document outpati encount review creat refer standard nlp train test vnlp framework use detect instanc lexic term compar refer standard posit predict valu extract relev concept studi demonstr feasibl extract posit assert concept relat homeless free text medic record

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