J Biomed Inform - Deriving consumer-facing disease concepts for family health histories using multi-source sampling.

Tópicos

{ health(1844) social(1437) communiti(874) }
{ concept(1167) ontolog(924) domain(897) }
{ ehr(2073) health(1662) electron(1139) }
{ sequenc(1873) structur(1644) protein(1328) }
{ patient(2315) diseas(1263) diabet(1191) }
{ data(1714) softwar(1251) tool(1186) }
{ use(976) code(926) identifi(902) }
{ import(1318) role(1303) understand(862) }
{ data(1737) use(1416) pattern(1282) }
{ health(3367) inform(1360) care(1135) }
{ search(2224) databas(1162) retriev(909) }
{ compound(1573) activ(1297) structur(1058) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ sampl(1606) size(1419) use(1276) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ take(945) account(800) differ(722) }
{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
{ studi(1410) differ(1259) use(1210) }
{ spatial(1525) area(1432) region(1030) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ cost(1906) reduc(1198) effect(832) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
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{ inform(2794) health(2639) internet(1427) }
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{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
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{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
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{ 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) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

The family health history has long been recognized as an effective way of understanding individuals' susceptibility to familial disease; yet electronic tools to support the capture and use of these data have been characterized as inadequate. As part of an ongoing effort to build patient-facing tools for entering detailed family health histories, we have compiled a set of concepts specific to familial disease using multi-source sampling. These concepts were abstracted by analyzing family health history data patterns in our enterprise data warehouse, collection patterns of consumer personal health records, analyses from the local state health department, a healthcare data dictionary, and concepts derived from genetic-oriented consumer education materials. Collectively, these sources yielded a set of more than 500 unique disease concepts, represented by more than 2500 synonyms for supporting patients in entering coded family health histories. We expect that these concepts will be useful in providing meaningful data and education resources for patients and providers alike.

Resumo Limpo

famili health histori long recogn effect way understand individu suscept famili diseas yet electron tool support captur use data character inadequ part ongo effort build patientfac tool enter detail famili health histori compil set concept specif famili diseas use multisourc sampl concept abstract analyz famili health histori data pattern enterpris data warehous collect pattern consum person health record analys local state health depart healthcar data dictionari concept deriv geneticori consum educ materi collect sourc yield set uniqu diseas concept repres synonym support patient enter code famili health histori expect concept will use provid meaning data educ resourc patient provid alik

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