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10.1093/jamia/ocab078 http://scihub22266oqcxt.onion/10.1093/jamia/ocab078 33895839!8135317!33895839     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33895839&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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+Am+Med+Inform+Assoc 2021 ; 28 (9): 1964-1969 Nephropedia Template TP {{tp|p=33895839|t=2021. COVID-19 trial graph: a linked graph for COVID-19 clinical trials |pdf=|usr=}}{{33895839}} gab.com Text COVID-19 trial graph: a linked graph for COVID-19 clinical trials JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33895839 #FOAvip OBJECTIVE: Clinical trials are an essential part of the effort to find safe and effective prevention and treatment for COVID-19. Given the rapid growth of COVID-19 clinical trials, there is an urgent need for a better clinical trial information retrieval tool that supports searching by specifying criteria, including both eligibility criteria and structured trial information. MATERIALS AND METHODS: We built a linked graph for registered COVID-19 clinical trials: the COVID-19 Trial Graph, to facilitate retrieval of clinical trials. Natural language processing tools were leveraged to extract and normalize the clinical trial information from both their eligibility criteria free texts and structured information from ClinicalTrials.gov. We linked the extracted data using the COVID-19 Trial Graph and imported it to a graph database, which supports both querying and visualization. We evaluated trial graph using case queries and graph embedding. RESULTS: The graph currently (as of October 5, 2020) contains 3392 registered COVID-19 clinical trials, with 17 480 nodes and 65 236 relationships. Manual evaluation of case queries found high precision and recall scores on retrieving relevant clinical trials searching from both eligibility criteria and trial-structured information. We observed clustering in clinical trials via graph embedding, which also showed superiority over the baseline (0.870 vs 0.820) in evaluating whether a trial can complete its recruitment successfully. CONCLUSIONS: The COVID-19 Trial Graph is a novel representation of clinical trials that allows diverse search queries and provides a graph-based visualization of COVID-19 clinical trials. High-dimensional vectors mapped by graph embedding for clinical trials would be potentially beneficial for many downstream applications, such as trial end recruitment status prediction and trial similarity comparison. Our methodology also is generalizable to other clinical trials. Twit Text FOAVip COVID-19 trial graph: a linked graph for COVID-19 clinical trials ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33895839 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33895839 #FOAvip English Wikipedia <ref>{{Cite pmid|33895839}}</ref> COVID-19 trial graph: a linked graph for COVID-19 clinical trials #MMPMID33895839 Du J; Wang Q; Wang J; Ramesh P; Xiang Y; Jiang X; Tao C J Am Med Inform Assoc 2021[Aug]; 28 (9): 1964-1969 PMID33895839show ga OBJECTIVE: Clinical trials are an essential part of the effort to find safe and effective prevention and treatment for COVID-19. Given the rapid growth of COVID-19 clinical trials, there is an urgent need for a better clinical trial information retrieval tool that supports searching by specifying criteria, including both eligibility criteria and structured trial information. MATERIALS AND METHODS: We built a linked graph for registered COVID-19 clinical trials: the COVID-19 Trial Graph, to facilitate retrieval of clinical trials. Natural language processing tools were leveraged to extract and normalize the clinical trial information from both their eligibility criteria free texts and structured information from ClinicalTrials.gov. We linked the extracted data using the COVID-19 Trial Graph and imported it to a graph database, which supports both querying and visualization. We evaluated trial graph using case queries and graph embedding. RESULTS: The graph currently (as of October 5, 2020) contains 3392 registered COVID-19 clinical trials, with 17 480 nodes and 65 236 relationships. Manual evaluation of case queries found high precision and recall scores on retrieving relevant clinical trials searching from both eligibility criteria and trial-structured information. We observed clustering in clinical trials via graph embedding, which also showed superiority over the baseline (0.870 vs 0.820) in evaluating whether a trial can complete its recruitment successfully. CONCLUSIONS: The COVID-19 Trial Graph is a novel representation of clinical trials that allows diverse search queries and provides a graph-based visualization of COVID-19 clinical trials. High-dimensional vectors mapped by graph embedding for clinical trials would be potentially beneficial for many downstream applications, such as trial end recruitment status prediction and trial similarity comparison. Our methodology also is generalizable to other clinical trials. |*COVID-19[MESH] |*Clinical Trials as Topic[MESH] |*Computer Graphics[MESH] |Cluster Analysis[MESH] |Databases, Factual[MESH] |Humans[MESH] |Natural Language Processing[MESH]COVID-19 trial graph: a linked graph for COVID-19 clinical trials (epmc).pdf DeepDyve Pubget Overpricing