J Biomed Inform - Comparing risks of alternative medical diagnosis using Bayesian arguments.

Tópicos

{ use(2086) technolog(871) perceiv(783) }
{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ problem(2511) optim(1539) algorithm(950) }
{ design(1359) user(1324) use(1319) }
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{ result(1111) use(1088) new(759) }
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{ record(1888) medic(1808) patient(1693) }
{ ehr(2073) health(1662) electron(1139) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
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{ patient(1821) servic(1111) care(1106) }
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{ cancer(2502) breast(956) screen(824) }
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{ estim(2440) model(1874) function(577) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

This paper explains the role of Bayes Theorem and Bayesian networks arising in a medical negligence case brought by a patient who suffered a stroke as a result of an invasive diagnostic test. The claim of negligence was based on the premise that an alternative (non-invasive) test should have been used because it carried a lower risk. The case raises a number of general and widely applicable concerns about the decision-making process within the medical profession, including the ethics of informed consent, patient care liabilities when errors are made, and the research problem of focusing on 'true positives' while ignoring 'false positives'. An immediate concern is how best to present Bayesian arguments in such a way that they can be understood by people who would normally balk at mathematical equations. We feel it is possible to present purely visual representations of a non-trivial Bayesian argument in such a way that no mathematical knowledge or understanding is needed. The approach supports a wide range of alternative scenarios, makes all assumptions easily understandable and offers significant potential benefits to many areas of medical decision-making.

Resumo Limpo

paper explain role bay theorem bayesian network aris medic neglig case brought patient suffer stroke result invas diagnost test claim neglig base premis altern noninvas test use carri lower risk case rais number general wide applic concern decisionmak process within medic profess includ ethic inform consent patient care liabil error made research problem focus true posit ignor fals posit immedi concern best present bayesian argument way can understood peopl normal balk mathemat equat feel possibl present pure visual represent nontrivi bayesian argument way mathemat knowledg understand need approach support wide rang altern scenario make assumpt easili understand offer signific potenti benefit mani area medic decisionmak

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