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10.1186/1472-6947-15-S1-S5 http://scihub22266oqcxt.onion/10.1186/1472-6947-15-S1-S5 C4460654!4460654!26043671     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       BMC+Med+Inform+Decis+Mak 2015 ; 15 (Suppl 1): S5 Nephropedia Template TP {{tp|p=26043671|t=2015. Injury narrative text classification using factorization model |pdf=|usr=}}{{26043671}} gab.com Text Injury narrative text classification using factorization model JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=26043671 #FOAvip Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93. Twit Text FOAVip Injury narrative text classification using factorization model ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=26043671 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26043671 #FOAvip English Wikipedia <ref>{{Cite pmid|26043671}}</ref> Injury narrative text classification using factorization model #MMPMID26043671 Chen L; Vallmuur K; Nayak R BMC Med Inform Decis Mak 2015[]; 15 (Suppl 1): S5 PMID26043671show ga Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93. äInjury narrative text classification using factorization model (epmc).pdf DeepDyve Pubget Overpricing