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http://scihub22266oqcxt.onion/ C4765649!4765649!26958301     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       AMIA+Annu+Symp+Proc 2015 ; 2015 (ä): 2015-24 Nephropedia Template TP {{tp|p=26958301|t=2015. Classification of Clinically Useful Sentences in MEDLINE |pdf=|usr=}}{{26958301}} gab.com Text Classification of Clinically Useful Sentences in MEDLINE JO: AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=26958301 #FOAvip Objective: In a previous study, we investigated a sentence classification model that uses semantic features to extract clinically useful sentences from UpToDate, a synthesized clinical evidence resource. In the present study, we assess the generalizability of the sentence classifier to Medline abstracts. Methods: We applied the classification model to an independent gold standard of high quality clinical studies from Medline. Then, the classifier trained on UpToDate sentences was optimized by re-retraining the classifier with Medline abstracts and adding a sentence location feature. Results: The previous classifier yielded an F-measure of 58% on Medline versus 67% on UpToDate. Re-training the classifier on Medline improved F-measure to 68%; and to 76% (p<0.01) after adding the sentence location feature. Conclusions: The classifier?s model and input features generalized to Medline abstracts, but the classifier needed to be retrained on Medline to achieve equivalent performance. Sentence location provided additional contribution to the overall classification performance. Twit Text FOAVip Classification of Clinically Useful Sentences in MEDLINE ????AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=26958301 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26958301 #FOAvip English Wikipedia <ref>{{Cite pmid|26958301}}</ref> Classification of Clinically Useful Sentences in MEDLINE #MMPMID26958301 Morid MA; Jonnalagadda S; Fiszman M; Raja K; Fiol GD AMIA Annu Symp Proc 2015[]; 2015 (ä): 2015-24 PMID26958301show ga Objective: In a previous study, we investigated a sentence classification model that uses semantic features to extract clinically useful sentences from UpToDate, a synthesized clinical evidence resource. In the present study, we assess the generalizability of the sentence classifier to Medline abstracts. Methods: We applied the classification model to an independent gold standard of high quality clinical studies from Medline. Then, the classifier trained on UpToDate sentences was optimized by re-retraining the classifier with Medline abstracts and adding a sentence location feature. Results: The previous classifier yielded an F-measure of 58% on Medline versus 67% on UpToDate. Re-training the classifier on Medline improved F-measure to 68%; and to 76% (p<0.01) after adding the sentence location feature. Conclusions: The classifier?s model and input features generalized to Medline abstracts, but the classifier needed to be retrained on Medline to achieve equivalent performance. Sentence location provided additional contribution to the overall classification performance. äClassification of Clinically Useful Sentences in MEDLINE (epmc).pdf DeepDyve Pubget Overpricing