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10.1155/2016/8313454 http://scihub22266oqcxt.onion/10.1155/2016/8313454 C5030470/?report=reader!5030470!27668260     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2016 ; 2016 (ä): ä Nephropedia Template TP {{tp|p=27668260|t=2016. Natural Language Processing Based Instrument for Classification of Free Text Medical Records |pdf=|usr=}}{{27668260}} gab.com Text Natural Language Processing Based Instrument for Classification of Free Text Medical Records JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27668260 #FOAvip According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a ?shrink? method, based on features selection, was introduced and applied. At the first stage of classification the results of the ?shrink? case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful. Twit Text FOAVip Natural Language Processing Based Instrument for Classification of Free Text Medical Records ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27668260 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27668260 #FOAvip English Wikipedia <ref>{{Cite pmid|27668260}}</ref> Natural Language Processing Based Instrument for Classification of Free Text Medical Records #MMPMID27668260 Khachidze M; Tsintsadze M; Archuadze M Biomed Res Int 2016[]; 2016 (ä): ä PMID27668260show ga According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a ?shrink? method, based on features selection, was introduced and applied. At the first stage of classification the results of the ?shrink? case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful. äNatural Language Processing Based Instrument for Classification of Fre(epmc).pdf DeepDyve Pubget Overpricing