Med Decis Making - Uncertainty and patient heterogeneity in medical decision models.

Tópicos

{ analysi(2126) use(1163) compon(1037) }
{ model(3404) distribut(989) bayesian(671) }
{ model(3480) simul(1196) paramet(876) }
{ decis(3086) make(1611) patient(1517) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ research(1218) medic(880) student(794) }
{ can(981) present(881) function(850) }
{ data(3963) clinic(1234) research(1004) }
{ motion(1329) object(1292) video(1091) }
{ research(1085) discuss(1038) issu(1018) }
{ record(1888) medic(1808) patient(1693) }
{ imag(1947) propos(1133) code(1026) }
{ imag(2830) propos(1344) filter(1198) }
{ patient(2315) diseas(1263) diabet(1191) }
{ concept(1167) ontolog(924) domain(897) }
{ control(1307) perform(991) simul(935) }
{ studi(1410) differ(1259) use(1210) }
{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ intervent(3218) particip(2042) group(1664) }
{ use(2086) technolog(871) perceiv(783) }
{ structur(1116) can(940) graph(676) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ process(1125) use(805) approach(778) }
{ detect(2391) sensit(1101) algorithm(908) }
{ 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) }
{ 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) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ 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) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ 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) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ 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) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ health(1844) social(1437) communiti(874) }
{ 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) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Parameter uncertainty, patient heterogeneity, and stochastic uncertainty of outcomes are increasingly important concepts in medical decision models. The purpose of this study is to demonstrate the various methods to analyze uncertainty and patient heterogeneity in a decision model. The authors distinguish various purposes of medical decision modeling, serving various stakeholders. Differences and analogies between the analyses are pointed out, as well as practical issues. The analyses are demonstrated with an example comparing imaging tests for patients with chest pain. For complicated analyses step-by-step algorithms are provided. The focus is on Monte Carlo simulation and value of information analysis. Increasing model complexity is a major challenge for probabilistic sensitivity analysis and value of information analysis. The authors discuss nested analyses that are required in patient-level models, and in nonlinear models for analyses of partial value of information analysis.

Resumo Limpo

paramet uncertainti patient heterogen stochast uncertainti outcom increas import concept medic decis model purpos studi demonstr various method analyz uncertainti patient heterogen decis model author distinguish various purpos medic decis model serv various stakehold differ analog analys point well practic issu analys demonstr exampl compar imag test patient chest pain complic analys stepbystep algorithm provid focus mont carlo simul valu inform analysi increas model complex major challeng probabilist sensit analysi valu inform analysi author discuss nest analys requir patientlevel model nonlinear model analys partial valu inform analysi

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