Res Synth Methods - Potential uses of Bayesian networks as tools for synthesis of systematic reviews of complex interventions.

Tópicos

{ studi(2440) review(1878) systemat(933) }
{ clinic(1479) use(1117) guidelin(835) }
{ decis(3086) make(1611) patient(1517) }
{ design(1359) user(1324) use(1319) }
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{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ howev(809) still(633) remain(590) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
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{ model(3404) distribut(989) bayesian(671) }
{ network(2748) neural(1063) input(814) }
{ general(901) number(790) one(736) }
{ visual(1396) interact(850) tool(830) }
{ research(1218) medic(880) student(794) }
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{ implement(1333) system(1263) develop(1122) }
{ detect(2391) sensit(1101) algorithm(908) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ 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) }
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{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
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{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention. Bayesian networks are useful extensions to logic maps when initiating a review or to facilitate synthesis and bridge the gap between evidence acquisition and decision-making. Formal elicitation techniques allow development of BNs on the basis of expert opinion. Such applications are useful alternatives to 'empty' reviews, which identify knowledge gaps but fail to support decision-making. Where review evidence exists, it can inform the development of a BN. We illustrate the construction of a BN using a motivating example that demonstrates how BNs can ensure coherence, transparently structure the problem addressed by a complex intervention and assess sensitivity to context, all of which are critical components of robust reviews of complex interventions. We suggest that BNs should be utilised to routinely synthesise reviews of complex interventions or empty reviews where decisions must be made despite poor evidence.

Resumo Limpo

bayesian network bns tool repres expert knowledg evid especi use synthesis evid belief concern complex intervent assess sensit outcom differ situat contextu framework frame decis problem involv altern type intervent bayesian network use extens logic map initi review facilit synthesi bridg gap evid acquisit decisionmak formal elicit techniqu allow develop bns basi expert opinion applic use altern empti review identifi knowledg gap fail support decisionmak review evid exist can inform develop bn illustr construct bn use motiv exampl demonstr bns can ensur coher transpar structur problem address complex intervent assess sensit context critic compon robust review complex intervent suggest bns utilis routin synthesis review complex intervent empti review decis must made despit poor evid

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