AMIA Annu Symp Proc - Application of Bayesian logistic regression to mining biomedical data.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ model(2341) predict(2261) use(1141) }
{ model(3404) distribut(989) bayesian(671) }
{ extract(1171) text(1153) clinic(932) }
{ model(2656) set(1616) predict(1553) }
{ studi(1410) differ(1259) use(1210) }
{ data(3008) multipl(1320) sourc(1022) }
{ analysi(2126) use(1163) compon(1037) }
{ use(976) code(926) identifi(902) }
{ patient(2315) diseas(1263) diabet(1191) }
{ assess(1506) score(1403) qualiti(1306) }
{ concept(1167) ontolog(924) domain(897) }
{ visual(1396) interact(850) tool(830) }
{ can(774) often(719) complex(702) }
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{ imag(2830) propos(1344) filter(1198) }
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{ 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) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
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{ drug(1928) target(777) effect(648) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Mining high dimensional biomedical data with existing classifiers is challenging and the predictions are often inaccurate. We investigated the use of Bayesian Logistic Regression (B-LR) for mining such data to predict and classify various disease conditions. The analysis was done on twelve biomedical datasets with binary class variables and the performance of B-LR was compared to those from other popular classifiers on these datasets with 10-fold cross validation using the WEKA data mining toolkit. The statistical significance of the results was analyzed by paired two tailed t-tests and non-parametric Wilcoxon signed-rank tests. We observed overall that B-LR with non-informative Gaussian priors performed on par with other classifiers in terms of accuracy, balanced accuracy and AUC. These results suggest that it is worthwhile to explore the application of B-LR to predictive modeling tasks in bioinformatics using informative biological prior probabilities. With informative prior probabilities, we conjecture that the performance of B-LR will improve.

Resumo Limpo

mine high dimension biomed data exist classifi challeng predict often inaccur investig use bayesian logist regress blr mine data predict classifi various diseas condit analysi done twelv biomed dataset binari class variabl perform blr compar popular classifi dataset fold cross valid use weka data mine toolkit statist signific result analyz pair two tail ttest nonparametr wilcoxon signedrank test observ overal blr noninform gaussian prior perform par classifi term accuraci balanc accuraci auc result suggest worthwhil explor applic blr predict model task bioinformat use inform biolog prior probabl inform prior probabl conjectur perform blr will improv

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