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10.1016/j.cie.2021.107236 http://scihub22266oqcxt.onion/10.1016/j.cie.2021.107236 33746344!7959675!33746344     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       Comput+Ind+Eng 2021 ; 156 (ä): 107236 Nephropedia Template TP {{tp|p=33746344|t=2021. EMR2vec: Bridging the gap between patient data and clinical trial |pdf=|usr=}}{{33746344}} gab.com Text EMR2vec: Bridging the gap between patient data and clinical trial JO: Comput Ind Eng http://www.kidney.de/pget.php?&tw=1&pmid=33746344 #FOAvip The human suffering from diseases caused by life-threatening viruses such as SARS, Ebola, and COVID-19 motivated many of us to study and discover the best means to harness the potential of data integration to assist clinical researchers to curb these viruses. Integrating patients data with clinical trials data is enormously promising as it provides a comprehensive knowledge base that accelerates the clinical research response-ability to tackle emerging infectious disease outbreaks. This work introduces EMR2vec, a platform that customises advanced NLP, machine learning and semantic web techniques to link potential patients to suitable clinical trials. Linking these two different but complementary datasets allows clinicians and researchers to compare patients to clinical research opportunities or to automatically select patients for personalized clinical care. The platform derives a 'bag of medical terms' (BoMT) from eligibility criteria by normalizing extracted entities through SNOMED-CT ontology. With the usage of BoMT, an ontological reasoning method is proposed to represent EMR and clinical trials in a vector space model. The platform presents a matching process that reduces vector dimensionality using a neural network, then applies orthogonality projection to measure the similarity between vectors. Finally, the proposed EMR2vec platform is evaluated with an extendable prototype based on Big data tools. Twit Text FOAVip EMR2vec: Bridging the gap between patient data and clinical trial ????Comput Ind Eng http://www.kidney.de/pget.php?&tw=1&pmid=33746344 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33746344 #FOAvip English Wikipedia <ref>{{Cite pmid|33746344}}</ref> EMR2vec: Bridging the gap between patient data and clinical trial #MMPMID33746344 Dhayne H; Kilany R; Haque R; Taher Y Comput Ind Eng 2021[Jun]; 156 (ä): 107236 PMID33746344show ga The human suffering from diseases caused by life-threatening viruses such as SARS, Ebola, and COVID-19 motivated many of us to study and discover the best means to harness the potential of data integration to assist clinical researchers to curb these viruses. Integrating patients data with clinical trials data is enormously promising as it provides a comprehensive knowledge base that accelerates the clinical research response-ability to tackle emerging infectious disease outbreaks. This work introduces EMR2vec, a platform that customises advanced NLP, machine learning and semantic web techniques to link potential patients to suitable clinical trials. Linking these two different but complementary datasets allows clinicians and researchers to compare patients to clinical research opportunities or to automatically select patients for personalized clinical care. The platform derives a 'bag of medical terms' (BoMT) from eligibility criteria by normalizing extracted entities through SNOMED-CT ontology. With the usage of BoMT, an ontological reasoning method is proposed to represent EMR and clinical trials in a vector space model. The platform presents a matching process that reduces vector dimensionality using a neural network, then applies orthogonality projection to measure the similarity between vectors. Finally, the proposed EMR2vec platform is evaluated with an extendable prototype based on Big data tools. äEMR2vec: Bridging the gap between patient data and clinical trial (epmc).pdf DeepDyve Pubget Overpricing