AMIA Annu Symp Proc - Quantifying the longitudinal value of healthcare record collections for pharmacoepidemiology.

Tópicos

{ assess(1506) score(1403) qualiti(1306) }
{ use(976) code(926) identifi(902) }
{ spatial(1525) area(1432) region(1030) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ data(3963) clinic(1234) research(1004) }
{ perform(1367) use(1326) method(1137) }
{ monitor(1329) mobil(1314) devic(1160) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ perform(999) metric(946) measur(919) }
{ data(1737) use(1416) pattern(1282) }
{ concept(1167) ontolog(924) domain(897) }
{ method(1557) propos(1049) approach(1037) }
{ visual(1396) interact(850) tool(830) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ first(2504) two(1366) second(1323) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ case(1353) use(1143) diagnosi(1136) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ research(1218) medic(880) student(794) }
{ group(2977) signific(1463) compar(1072) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ 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) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ health(1844) social(1437) communiti(874) }
{ 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(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We introduce an information score for longitudinal healthcare record data, specifically in the monitoring of chronic conditions. The score is designed to capture the value of different observation patterns in terms of shaping and testing clinical epidemiological hypotheses. The score is first developed for the simple case where equally spaced observations are most informative, then extended to a more context-specific version where the optimal density of observations can be elicited. It can be interpreted as a measure of the average quantity of information provided by each observation in an individual's time course, where information is lost whenever the observation density deviates from a defined optimal density. We illustrate the score on routine healthcare records from the population of Salford, UK - focusing on repeat testing of liver function in people with common long-term conditions. We demonstrate validity of the score in terms of concordance between score levels and clinically meaningful patterns of repeat testing.

Resumo Limpo

introduc inform score longitudin healthcar record data specif monitor chronic condit score design captur valu differ observ pattern term shape test clinic epidemiolog hypothes score first develop simpl case equal space observ inform extend contextspecif version optim densiti observ can elicit can interpret measur averag quantiti inform provid observ individu time cours inform lost whenev observ densiti deviat defin optim densiti illustr score routin healthcar record popul salford uk focus repeat test liver function peopl common longterm condit demonstr valid score term concord score level clinic meaning pattern repeat test

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