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http://scihub22266oqcxt.onion/ C4765588!4765588!26958209     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 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       AMIA+Annu+Symp+Proc 2015 ; 2015 (ä): 737-46 Nephropedia Template TP {{tp|p=26958209|t=2015. A Study of Concept Extraction Across Different Types of Clinical Notes |pdf=|usr=}}{{26958209}} gab.com Text A Study of Concept Extraction Across Different Types of Clinical Notes JO: AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=26958209 #FOAvip Our research investigates methods for creating effective concept extractors for specialty clinical notes. First, we present three new ?specialty area? datasets consisting of Cardiology, Neurology, and Orthopedics clinical notes manually annotated with medical concepts. We analyze the medical concepts in each dataset and compare with the widely used i2b2 2010 corpus. Second, we create several types of concept extraction models and examine the effects of training supervised learners with specialty area data versus i2b2 data. We find substantial differences in performance across the datasets, and obtain the best results for all three specialty areas by training with both i2b2 and specialty data. Third, we explore strategies to improve concept extraction on specialty notes with ensemble methods. We compare two types of ensemble methods (Voting/Stacking) and a domain adaptation model, and show that a Stacked ensemble of classifiers trained with i2b2 and specialty data yields the best performance. Twit Text FOAVip A Study of Concept Extraction Across Different Types of Clinical Notes ????AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=26958209 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26958209 #FOAvip English Wikipedia <ref>{{Cite pmid|26958209}}</ref> A Study of Concept Extraction Across Different Types of Clinical Notes #MMPMID26958209 Kim Y; Riloff E; Hurdle JF AMIA Annu Symp Proc 2015[]; 2015 (ä): 737-46 PMID26958209show ga Our research investigates methods for creating effective concept extractors for specialty clinical notes. First, we present three new ?specialty area? datasets consisting of Cardiology, Neurology, and Orthopedics clinical notes manually annotated with medical concepts. We analyze the medical concepts in each dataset and compare with the widely used i2b2 2010 corpus. Second, we create several types of concept extraction models and examine the effects of training supervised learners with specialty area data versus i2b2 data. We find substantial differences in performance across the datasets, and obtain the best results for all three specialty areas by training with both i2b2 and specialty data. Third, we explore strategies to improve concept extraction on specialty notes with ensemble methods. We compare two types of ensemble methods (Voting/Stacking) and a domain adaptation model, and show that a Stacked ensemble of classifiers trained with i2b2 and specialty data yields the best performance. äA Study of Concept Extraction Across Different Types of Clinical Notes(epmc).pdf DeepDyve Pubget Overpricing