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http://scihub22266oqcxt.onion/ C4765685!4765685!26958170     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 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): 386-95 Nephropedia Template TP {{tp|p=26958170|t=2015. Similarity-Based Recommendation of New Concepts to a Terminology |pdf=|usr=}}{{26958170}} gab.com Text Similarity-Based Recommendation of New Concepts to a Terminology JO: AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=26958170 #FOAvip Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation. Twit Text FOAVip Similarity-Based Recommendation of New Concepts to a Terminology ????AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=26958170 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26958170 #FOAvip English Wikipedia <ref>{{Cite pmid|26958170}}</ref> Similarity-Based Recommendation of New Concepts to a Terminology #MMPMID26958170 Chandar P; Yaman A; Hoxha J; He Z; Weng C AMIA Annu Symp Proc 2015[]; 2015 (ä): 386-95 PMID26958170show ga Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation. äSimilarity-Based Recommendation of New Concepts to a Terminology (epmc).pdf DeepDyve Pubget Overpricing