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10.1186/s13326-015-0019-z http://scihub22266oqcxt.onion/10.1186/s13326-015-0019-z C4450611!4450611!26034558     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       J+Biomed+Semantics 2015 ; 6 (ä): ä Nephropedia Template TP {{tp|p=26034558|t=2015. Concept selection for phenotypes and diseases using learn to rank |pdf=|usr=}}{{26034558}} gab.com Text Concept selection for phenotypes and diseases using learn to rank JO: J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=26034558 #FOAvip Background: Phenotypes form the basis for determining the existence of a disease against the given evidence. Much of this evidence though remains locked away in text ? scientific articles, clinical trial reports and electronic patient records (EPR) ? where authors use the full expressivity of human language to report their observations. Results: In this paper we exploit a combination of off-the-shelf tools for extracting a machine understandable representation of phenotypes and other related concepts that concern the diagnosis and treatment of diseases. These are tested against a gold standard EPR collection that has been annotated with Unified Medical Language System (UMLS) concept identifiers: the ShARE/CLEF 2013 corpus for disorder detection. We evaluate four pipelines as stand-alone systems and then attempt to optimise semantic-type based performance using several learn-to-rank (LTR) approaches ? three pairwise and one listwise. We observed that whilst overall Apache cTAKES tended to outperform other stand-alone systems on a strong recall (R = 0.57), precision was low (P = 0.09) leading to low-to-moderate F1 measure (F1 = 0.16). Moreover, there is substantial variation in system performance across semantic types for disorders. For example, the concept Findings (T033) seemed to be very challenging for all systems. Combining systems within LTR improved F1 substantially (F1 = 0.24) particularly for Disease or syndrome (T047) and Anatomical abnormality (T190). Whilst recall is improved markedly, precision remains a challenge (P = 0.15, R = 0.59). Twit Text FOAVip Concept selection for phenotypes and diseases using learn to rank ????J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=26034558 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26034558 #FOAvip English Wikipedia <ref>{{Cite pmid|26034558}}</ref> Concept selection for phenotypes and diseases using learn to rank #MMPMID26034558 Collier N; Oellrich A; Groza T J Biomed Semantics 2015[]; 6 (ä): ä PMID26034558show ga Background: Phenotypes form the basis for determining the existence of a disease against the given evidence. Much of this evidence though remains locked away in text ? scientific articles, clinical trial reports and electronic patient records (EPR) ? where authors use the full expressivity of human language to report their observations. Results: In this paper we exploit a combination of off-the-shelf tools for extracting a machine understandable representation of phenotypes and other related concepts that concern the diagnosis and treatment of diseases. These are tested against a gold standard EPR collection that has been annotated with Unified Medical Language System (UMLS) concept identifiers: the ShARE/CLEF 2013 corpus for disorder detection. We evaluate four pipelines as stand-alone systems and then attempt to optimise semantic-type based performance using several learn-to-rank (LTR) approaches ? three pairwise and one listwise. We observed that whilst overall Apache cTAKES tended to outperform other stand-alone systems on a strong recall (R = 0.57), precision was low (P = 0.09) leading to low-to-moderate F1 measure (F1 = 0.16). Moreover, there is substantial variation in system performance across semantic types for disorders. For example, the concept Findings (T033) seemed to be very challenging for all systems. Combining systems within LTR improved F1 substantially (F1 = 0.24) particularly for Disease or syndrome (T047) and Anatomical abnormality (T190). Whilst recall is improved markedly, precision remains a challenge (P = 0.15, R = 0.59). äConcept selection for phenotypes and diseases using learn to rank (epmc).pdf DeepDyve Pubget Overpricing