COVID-19 Trial Graph: A Linked Graph for COVID-19 Clinical Trials.
covid-19, Clinical trial, eligibility criteria, graph representation
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 that supports searching by specifying criteria including both eligibility criteria and structured trial information. 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 (NLP) 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 query and visualization. We evaluated trial graph using case queries and graph embedding. The graph currently (as of 10-05-2020) contains 3,392 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 (0.8704 vs. 0.8199) in evaluating whether a trial can complete its recruitment successfully. 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, such as cancer clinical trials.
Jingcheng Du, Qing Wang, Jingqi Wang, Prerana Ramesh, Yang Xiang, Xiaoqian Jiang, Cui Tao
Journal of the American Medical Informatics Association : JAMIA