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10.4338/ACI-2014-11-RA-0110 http://scihub22266oqcxt.onion/10.4338/ACI-2014-11-RA-0110 C4586346!4586346!26448801     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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Appl+Clin+Inform 2015 ; 6 (3): 600-110 Nephropedia Template TP {{tp|p=26448801|t=2015. Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports |pdf=|usr=}}{{26448801}} gab.com Text Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports JO: Appl Clin Inform http://www.kidney.de/pget.php?&tw=1&pmid=26448801 #FOAvip Background: Accumulating quantitative outcome parameters may contribute to constructing a healthcare organization in which outcomes of clinical procedures are reproducible and predictable. In imaging studies, measurements are the principal category of quantitative para meters. Objectives: The purpose of this work is to develop and evaluate two natural language processing engines that extract finding and organ measurements from narrative radiology reports and to categorize extracted measurements by their ?temporality?. Methods: The measurement extraction engine is developed as a set of regular expressions. The engine was evaluated against a manually created ground truth. Automated categorization of measurement temporality is defined as a machine learning problem. A ground truth was manually developed based on a corpus of radiology reports. A maximum entropy model was created using features that characterize the measurement itself and its narrative context. The model was evaluated in a ten-fold cross validation protocol. Results: The measurement extraction engine has precision 0.994 and recall 0.991. Accuracy of the measurement classification engine is 0.960. Conclusions: The work contributes to machine understanding of radiology reports and may find application in software applications that process medical data. Twit Text FOAVip Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports ????Appl Clin Inform http://www.kidney.de/pget.php?&tw=1&pmid=26448801 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26448801 #FOAvip English Wikipedia <ref>{{Cite pmid|26448801}}</ref> Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports #MMPMID26448801 Sevenster M; Buurman J; Liu P; Peters J; Chang P Appl Clin Inform 2015[]; 6 (3): 600-110 PMID26448801show ga Background: Accumulating quantitative outcome parameters may contribute to constructing a healthcare organization in which outcomes of clinical procedures are reproducible and predictable. In imaging studies, measurements are the principal category of quantitative para meters. Objectives: The purpose of this work is to develop and evaluate two natural language processing engines that extract finding and organ measurements from narrative radiology reports and to categorize extracted measurements by their ?temporality?. Methods: The measurement extraction engine is developed as a set of regular expressions. The engine was evaluated against a manually created ground truth. Automated categorization of measurement temporality is defined as a machine learning problem. A ground truth was manually developed based on a corpus of radiology reports. A maximum entropy model was created using features that characterize the measurement itself and its narrative context. The model was evaluated in a ten-fold cross validation protocol. Results: The measurement extraction engine has precision 0.994 and recall 0.991. Accuracy of the measurement classification engine is 0.960. Conclusions: The work contributes to machine understanding of radiology reports and may find application in software applications that process medical data. äNatural Language Processing Techniques for Extracting and Categorizing(epmc).pdf DeepDyve Pubget Overpricing