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10.1186/s13643-015-0066-7 http://scihub22266oqcxt.onion/10.1186/s13643-015-0066-7 C4514954!4514954!26073888     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Syst+Rev 2015 ; 4 (ä): ä Nephropedia Template TP {{tp|p=26073888|t=2015. Automating data extraction in systematic reviews: a systematic review |pdf=|usr=}}{{26073888}} gab.com Text Automating data extraction in systematic reviews: a systematic review JO: Syst Rev http://www.kidney.de/pget.php?&tw=1&pmid=26073888 #FOAvip Background: Automation of the parts of systematic review process, specifically the data extraction step, may be an important strategy to reduce the time necessary to complete a systematic review. However, the state of the science of automatically extracting data elements from full texts has not been well described. This paper performs a systematic review of published and unpublished methods to automate data extraction for systematic reviews. Methods: We systematically searched PubMed, IEEEXplore, and ACM Digital Library to identify potentially relevant articles. We included reports that met the following criteria: 1) methods or results section described what entities were or need to be extracted, and 2) at least one entity was automatically extracted with evaluation results that were presented for that entity. We also reviewed the citations from included reports. Results: Out of a total of 1190 unique citations that met our search criteria, we found 26 published reports describing automatic extraction of at least one of more than 52 potential data elements used in systematic reviews. For 25 (48 %) of the data elements used in systematic reviews, there were attempts from various researchers to extract information automatically from the publication text. Out of these, 14 (27 %) data elements were completely extracted, but the highest number of data elements extracted automatically by a single study was 7. Most of the data elements were extracted with F-scores (a mean of sensitivity and positive predictive value) of over 70 %. Conclusions: We found no unified information extraction framework tailored to the systematic review process, and published reports focused on a limited (1?7) number of data elements. Biomedical natural language processing techniques have not been fully utilized to fully or even partially automate the data extraction step of systematic reviews. Twit Text FOAVip Automating data extraction in systematic reviews: a systematic review ????Syst Rev http://www.kidney.de/pget.php?&tw=1&pmid=26073888 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26073888 #FOAvip English Wikipedia <ref>{{Cite pmid|26073888}}</ref> Automating data extraction in systematic reviews: a systematic review #MMPMID26073888 Jonnalagadda SR; Goyal P; Huffman MD Syst Rev 2015[]; 4 (ä): ä PMID26073888show ga Background: Automation of the parts of systematic review process, specifically the data extraction step, may be an important strategy to reduce the time necessary to complete a systematic review. However, the state of the science of automatically extracting data elements from full texts has not been well described. This paper performs a systematic review of published and unpublished methods to automate data extraction for systematic reviews. Methods: We systematically searched PubMed, IEEEXplore, and ACM Digital Library to identify potentially relevant articles. We included reports that met the following criteria: 1) methods or results section described what entities were or need to be extracted, and 2) at least one entity was automatically extracted with evaluation results that were presented for that entity. We also reviewed the citations from included reports. Results: Out of a total of 1190 unique citations that met our search criteria, we found 26 published reports describing automatic extraction of at least one of more than 52 potential data elements used in systematic reviews. For 25 (48 %) of the data elements used in systematic reviews, there were attempts from various researchers to extract information automatically from the publication text. Out of these, 14 (27 %) data elements were completely extracted, but the highest number of data elements extracted automatically by a single study was 7. Most of the data elements were extracted with F-scores (a mean of sensitivity and positive predictive value) of over 70 %. Conclusions: We found no unified information extraction framework tailored to the systematic review process, and published reports focused on a limited (1?7) number of data elements. Biomedical natural language processing techniques have not been fully utilized to fully or even partially automate the data extraction step of systematic reviews. äAutomating data extraction in systematic reviews: a systematic review (epmc).pdf DeepDyve Pubget Overpricing