J Biomed Inform - Common data model for natural language processing based on two existing standard information models: CDA+GrAF.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ concept(1167) ontolog(924) domain(897) }
{ control(1307) perform(991) simul(935) }
{ model(2341) predict(2261) use(1141) }
{ use(976) code(926) identifi(902) }
{ data(2317) use(1299) case(1017) }
{ data(3008) multipl(1320) sourc(1022) }
{ process(1125) use(805) approach(778) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ design(1359) user(1324) use(1319) }
{ model(2656) set(1616) predict(1553) }
{ method(1219) similar(1157) match(930) }
{ framework(1458) process(801) describ(734) }
{ health(1844) social(1437) communiti(874) }
{ activ(1452) weight(1219) physic(1104) }
{ imag(1947) propos(1133) code(1026) }
{ imag(1057) registr(996) error(939) }
{ network(2748) neural(1063) input(814) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ general(901) number(790) one(736) }
{ research(1085) discuss(1038) issu(1018) }
{ cost(1906) reduc(1198) effect(832) }
{ intervent(3218) particip(2042) group(1664) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ result(1111) use(1088) new(759) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ 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) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ 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) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

An increasing need for collaboration and resources sharing in the Natural Language Processing (NLP) research and development community motivates efforts to create and share a common data model and a common terminology for all information annotated and extracted from clinical text. We have combined two existing standards: the HL7 Clinical Document Architecture (CDA), and the ISO Graph Annotation Format (GrAF; in development), to develop such a data model entitled "CDA+GrAF". We experimented with several methods to combine these existing standards, and eventually selected a method wrapping separate CDA and GrAF parts in a common standoff annotation (i.e., separate from the annotated text) XML document. Two use cases, clinical document sections, and the 2010 i2b2/VA NLP Challenge (i.e., problems, tests, and treatments, with their assertions and relations), were used to create examples of such standoff annotation documents, and were successfully validated with the XML schemata provided with both standards. We developed a tool to automatically translate annotation documents from the 2010 i2b2/VA NLP Challenge format to GrAF, and automatically generated 50 annotation documents using this tool, all successfully validated. Finally, we adapted the XSL stylesheet provided with HL7 CDA to allow viewing annotation XML documents in a web browser, and plan to adapt existing tools for translating annotation documents between CDA+GrAF and the UIMA and GATE frameworks. This common data model may ease directly comparing NLP tools and applications, combining their output, transforming and "translating" annotations between different NLP applications, and eventually "plug-and-play" of different modules in NLP applications.

Resumo Limpo

increas need collabor resourc share natur languag process nlp research develop communiti motiv effort creat share common data model common terminolog inform annot extract clinic text combin two exist standard hl clinic document architectur cda iso graph annot format graf develop develop data model entitl cdagraf experi sever method combin exist standard eventu select method wrap separ cda graf part common standoff annot ie separ annot text xml document two use case clinic document section ibva nlp challeng ie problem test treatment assert relat use creat exampl standoff annot document success valid xml schemata provid standard develop tool automat translat annot document ibva nlp challeng format graf automat generat annot document use tool success valid final adapt xsl stylesheet provid hl cda allow view annot xml document web browser plan adapt exist tool translat annot document cdagraf uima gate framework common data model may eas direct compar nlp tool applic combin output transform translat annot differ nlp applic eventu plugandplay differ modul nlp applic

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