J Biomed Inform - A biological continuum based approach for efficient clinical classification.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ extract(1171) text(1153) clinic(932) }
{ risk(3053) factor(974) diseas(938) }
{ framework(1458) process(801) describ(734) }
{ method(2212) result(1239) propos(1039) }
{ measur(2081) correl(1212) valu(896) }
{ can(774) often(719) complex(702) }
{ network(2748) neural(1063) input(814) }
{ method(1557) propos(1049) approach(1037) }
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{ perform(1367) use(1326) method(1137) }
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{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
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{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ first(2504) two(1366) second(1323) }
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{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

Clinical feature selection problem is the task of selecting and identifying a subset of informative clinical features that are useful for promoting accurate clinical diagnosis. This is a significant task of pragmatic value in the clinical settings as each clinical test is associated with a different financial cost, diagnostic value, and risk for obtaining the measurement. Moreover, with continual introduction of new clinical features, the need to repeat the feature selection task can be very time consuming. Therefore to address this issue, we propose a novel feature selection technique for diagnosis of myocardial infarction - one of the leading causes of morbidity and mortality in many high-income countries. This method adopts the conceptual framework of biological continuum, the optimization capability of genetic algorithm for performing feature selection and the classification ability of support vector machine. Together, a network of clinical risk factors, called the biological continuum based etiological network (BCEN), was constructed. Evaluation of the proposed methods was carried out using the cardiovascular heart study (CHS) dataset. Results demonstrate a significant speedup of 4.73-fold can be achieved for the development of MI classification model. The key advantage of this methodology is the provision of a reusable (feature subset) paradigm for efficient development of up-to-date and efficacious clinical classification models.

Resumo Limpo

clinic featur select problem task select identifi subset inform clinic featur use promot accur clinic diagnosi signific task pragmat valu clinic set clinic test associ differ financi cost diagnost valu risk obtain measur moreov continu introduct new clinic featur need repeat featur select task can time consum therefor address issu propos novel featur select techniqu diagnosi myocardi infarct one lead caus morbid mortal mani highincom countri method adopt conceptu framework biolog continuum optim capabl genet algorithm perform featur select classif abil support vector machin togeth network clinic risk factor call biolog continuum base etiolog network bcen construct evalu propos method carri use cardiovascular heart studi chs dataset result demonstr signific speedup fold can achiev develop mi classif model key advantag methodolog provis reusabl featur subset paradigm effici develop uptod efficaci clinic classif model

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