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10.1186/s12911-017-0429-1 http://scihub22266oqcxt.onion/10.1186/s12911-017-0429-1 C5390441!5390441!28407816     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Inform+Decis+Mak 2017 ; 17 (ä): ä Nephropedia Template TP {{tp|p=28407816|t=2017. Automatic identification of variables in epidemiological datasets using logic regression |pdf=|usr=}}{{28407816}} gab.com Text Automatic identification of variables in epidemiological datasets using logic regression JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=28407816 #FOAvip Background: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies. Electronic supplementary material: The online version of this article (doi:10.1186/s12911-017-0429-1) contains supplementary material, which is available to authorized users. Twit Text FOAVip Automatic identification of variables in epidemiological datasets using logic regression ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=28407816 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28407816 #FOAvip English Wikipedia <ref>{{Cite pmid|28407816}}</ref> Automatic identification of variables in epidemiological datasets using logic regression #MMPMID28407816 Lorenz MW; Abdi NA; Scheckenbach F; Pflug A; Bülbül A; Catapano AL; Agewall S; Ezhov M; Bots ML; Kiechl S; Orth A BMC Med Inform Decis Mak 2017[]; 17 (ä): ä PMID28407816show ga Background: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies. Electronic supplementary material: The online version of this article (doi:10.1186/s12911-017-0429-1) contains supplementary material, which is available to authorized users. äAutomatic identification of variables in epidemiological datasets usin(epmc).pdf DeepDyve Pubget Overpricing