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10.1016/j.jbi.2021.103790 http://scihub22266oqcxt.onion/10.1016/j.jbi.2021.103790 33887457!8079156!33887457     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33887457&cmd=llinks): Failed to open stream: HTTP request failed! 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Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials |pdf=|usr=}}{{33887457}} gab.com Text Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=33887457 #FOAvip Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity: i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction. Twit Text FOAVip Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=33887457 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33887457 #FOAvip English Wikipedia <ref>{{Cite pmid|33887457}}</ref> Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials #MMPMID33887457 Sun Y; Butler A; Stewart LA; Liu H; Yuan C; Southard CT; Kim JH; Weng C J Biomed Inform 2021[Jun]; 118 (�): 103790 PMID33887457show ga Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity: i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction. |*COVID-19[MESH] |*Clinical Trials as Topic[MESH] |*Computer Simulation[MESH] |*Eligibility Determination[MESH] |Humans[MESH] |Machine Learning[MESH]Building an OMOP common data model-compliant annotated corpus for COVI(epmc).pdf DeepDyve Pubget Overpricing