Methods Inf Med - Secure Secondary Use of Clinical Data with Cloud-based NLP Services. Towards a Highly Scalable Research Infrastructure.

Tópicos

{ data(1714) softwar(1251) tool(1186) }
{ extract(1171) text(1153) clinic(932) }
{ data(1737) use(1416) pattern(1282) }
{ sequenc(1873) structur(1644) protein(1328) }
{ medic(1828) order(1363) alert(1069) }
{ framework(1458) process(801) describ(734) }
{ health(3367) inform(1360) care(1135) }
{ patient(2837) hospit(1953) medic(668) }
{ activ(1138) subject(705) human(624) }
{ process(1125) use(805) approach(778) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(2317) use(1299) case(1017) }
{ first(2504) two(1366) second(1323) }
{ design(1359) user(1324) use(1319) }
{ system(1050) medic(1026) inform(1018) }
{ data(3008) multipl(1320) sourc(1022) }
{ analysi(2126) use(1163) compon(1037) }
{ implement(1333) system(1263) develop(1122) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ research(1085) discuss(1038) issu(1018) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ surgeri(1148) surgic(1085) robot(1054) }
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{ error(1145) method(1030) estim(1020) }
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{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ import(1318) role(1303) understand(862) }
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{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ 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(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ 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) }
{ method(2212) result(1239) propos(1039) }

Resumo

JECTIVES: The secondary use of clinical data provides large opportunities for clinical and translational research as well as quality assurance projects. For such purposes, it is necessary to provide a flexible and scalable infrastructure that is compliant with privacy requirements. The major goals of the cloud4health project are to define such an architecture, to implement a technical prototype that fulfills these requirements and to evaluate it with three use cases.METHODS: The architecture provides components for multiple data provider sites such as hospitals to extract free text as well as structured data from local sources and de-identify such data for further anonymous or pseudonymous processing. Free text documentation is analyzed and transformed into structured information by text-mining services, which are provided within a cloud-computing environment. Thus, newly gained annotations can be integrated along with the already available structured data items and the resulting data sets can be uploaded to a central study portal for further analysis.RESULTS: Based on the architecture design, a prototype has been implemented and is under evaluation in three clinical use cases. Data from several hundred patients provided by a University Hospital and a private hospital chain have already been processed.CONCLUSIONS: Cloud4health has shown how existing components for secondary use of structured data can be complemented with text-mining in a privacy compliant manner. The cloud-computing paradigm allows a flexible and dynamically adaptable service provision that facilitates the adoption of services by data providers without own investments in respective hardware resources and software tools.

Resumo Limpo

jectiv secondari use clinic data provid larg opportun clinic translat research well qualiti assur project purpos necessari provid flexibl scalabl infrastructur compliant privaci requir major goal cloudhealth project defin architectur implement technic prototyp fulfil requir evalu three use casesmethod architectur provid compon multipl data provid site hospit extract free text well structur data local sourc deidentifi data anonym pseudonym process free text document analyz transform structur inform textmin servic provid within cloudcomput environ thus newli gain annot can integr along alreadi avail structur data item result data set can upload central studi portal analysisresult base architectur design prototyp implement evalu three clinic use case data sever hundr patient provid univers hospit privat hospit chain alreadi processedconclus cloudhealth shown exist compon secondari use structur data can complement textmin privaci compliant manner cloudcomput paradigm allow flexibl dynam adapt servic provis facilit adopt servic data provid without invest respect hardwar resourc softwar tool

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