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Clinical trials are conducted to test the effectiveness and safety ofpotential drugs in humans for regulatory approval. Machine learning (ML) hasrecently emerged as a new tool to assist in clinical trials. Despite thisprogress, there have been few efforts to document and benchmark ML4Trialalgorithms available to the ML research community. Additionally, theaccessibility to clinical trial-related datasets is limited, and there is alack of well-defined clinical tasks to facilitate the development of newalgorithms.  To fill this gap, we have developed PyTrial that provides benchmarks andopen-source implementations of a series of ML algorithms for clinical trialdesign and operations. In this paper, we thoroughly investigate 34 MLalgorithms for clinical trials across 6 different tasks, including patientoutcome prediction, trial site selection, trial outcome prediction,patient-trial matching, trial similarity search, and synthetic data generation.We have also collected and prepared 23 ML-ready datasets as well as theirworking examples in Jupyter Notebooks for quick implementation and testing.  PyTrial defines each task through a simple four-step process: data loading,model specification, model training, and model evaluation, all achievable withjust a few lines of code. Furthermore, our modular API architecture empowerspractitioners to expand the framework to incorporate new algorithms and taskseffortlessly. The code is available at https://github.com/RyanWangZf/PyTrial.

PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications