© 2024 Elsevier LtdClinicalTrials.gov hosts an online database with over 440,000 medical studies (as of 2023) evaluating drugs, supplements, medical devices, and behavioral treatments. Target users include scientists, medical researchers, pharmaceutical companies, and other public and private institutions. Although ClinicalTrials has some filtering ability, it does not provide visualization tools, reporting tools or historical data; only the most recent state of each trial is visible to users. To fill these functionality gaps, we present Tri-AL: an open-source data platform for clinical trial visualization, information extraction, historical analysis, and reporting. This paper describes the design and functionality of Tri-AL, including a programmable module to incorporate machine learning models and extract disease-specific data from unstructured trial reports, which we demonstrate using Alzheimer's disease reporting as a case study. We also highlight the use of Tri-AL for trial participation analysis in terms of sex, gender, race and ethnicity. The source code is publicly available at https://github.com/pouyan9675/Tri-AL.
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