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10.1186/1472-6947-15-S1-S8 http://scihub22266oqcxt.onion/10.1186/1472-6947-15-S1-S8 C4460622!4460622!26043839     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 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): S8 Nephropedia Template TP {{tp|p=26043839|t=2015. A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest |pdf=|usr=}}{{26043839}} gab.com Text A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=26043839 #FOAvip Background: Electronic Health Records (EHRs) are frequently used by clinicians and researchers to search for, extract, and analyze groups of patients by defining Health Outcome of Interests (HOI). The definition of an HOI is generally considered a complex and time consuming task for health care professionals. Methods: In our clinical note-based pharmacovigilance research, we often operate upon potentially hundreds of ontologies at once, expand query inputs, and we also increase the search space over clinical text as well as structured data. Such a method implies to specify an initial set of seed concepts, which are based on concept unique identifiers. This paper presents a novel method based on Formal Concept Analysis (FCA) and Semantic Query Expansion (SQE) to assist the end-user in defining their seed queries and in refining the expanded search space that it encompasses. Results: We evaluate our method over a gold-standard corpus from the 2008 i2b2 Obesity Challenge. This experimentation emphasizes positive results for sensitivity and specificity measures. Our new approach provides better recall with high precision of the obtained results. The most promising aspect of this approach consists in the discovery of positive results not present our Obesity NLP reference set. Conclusions: Together with a Web graphical user interface, our FCA and SQE cooperation end up being an efficient approach for refining health outcome of interest using plain terms. We consider that this approach can be extended to support other domains such as cohort building tools. Twit Text FOAVip A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=26043839 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26043839 #FOAvip English Wikipedia <ref>{{Cite pmid|26043839}}</ref> A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest #MMPMID26043839 Curé OC; Maurer H; Shah NH; Le Pendu P BMC Med Inform Decis Mak 2015[]; 15 (Suppl 1): S8 PMID26043839show ga Background: Electronic Health Records (EHRs) are frequently used by clinicians and researchers to search for, extract, and analyze groups of patients by defining Health Outcome of Interests (HOI). The definition of an HOI is generally considered a complex and time consuming task for health care professionals. Methods: In our clinical note-based pharmacovigilance research, we often operate upon potentially hundreds of ontologies at once, expand query inputs, and we also increase the search space over clinical text as well as structured data. Such a method implies to specify an initial set of seed concepts, which are based on concept unique identifiers. This paper presents a novel method based on Formal Concept Analysis (FCA) and Semantic Query Expansion (SQE) to assist the end-user in defining their seed queries and in refining the expanded search space that it encompasses. Results: We evaluate our method over a gold-standard corpus from the 2008 i2b2 Obesity Challenge. This experimentation emphasizes positive results for sensitivity and specificity measures. Our new approach provides better recall with high precision of the obtained results. The most promising aspect of this approach consists in the discovery of positive results not present our Obesity NLP reference set. Conclusions: Together with a Web graphical user interface, our FCA and SQE cooperation end up being an efficient approach for refining health outcome of interest using plain terms. We consider that this approach can be extended to support other domains such as cohort building tools. äA formal concept analysis and semantic query expansion cooperation to (epmc).pdf DeepDyve Pubget Overpricing