As machine learning techniques and artifical intelligence become more widely available and used, they are being explored in the context of gathering clinical trial data.
Du J, Wang Q, Wang J et al. COVID-19 Trial Graph: A Linked Graph for COVID-19 Clinical Trials. Journal of the American Medical Informatics Association. 2021. DOI: 10.1093/jamia/ocab078.
The authors built a "COVID-19 Trial Graph, a graph-based clinical trial data repository, to link structured and unstructured (ie, eligibility criteria) information for existing registered COVID-19 clinical trials. The COVID-19 Trial Graph supports diverse search queries with a particular focus on eligibility criteria and provides a graph-based visualization of COVID-19 clinical trials."
https://github.com/UT-Tao-group/clinical_trial_graph
Elghafari A, Finkelstein J. Automated Identification of Common Disease-Specific Outcomes for Comparative Effectiveness Research Using ClinicalTrials.gov: Algorithm Development and Validation Study. JMIR Med Inform 2021;9 (2): e18298. DOI: 10.2196/18298
The authors built a query pipeline with ClinicalTrials.gov to obtain lists of outcome measures used in trials of a specific condition.
Fanshawe T R, Perera, R. Automatic extraction of quantitative data from ClinicalTrials.gov to conduct meta-analyses. BMJ Evid Based Med 2020;25 (3): 113–114. DOI: 10.1136/bmjebm-2019-111206.
A Python-based software application (EXACT) that automatically extracts data required for meta-analysis from the ClinicalTrials.gov database in a spreadsheet format.
Updated 5 Oct 2021