# PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications

arXiv: https://arxiv.org/abs/2306.04018 · HTML: https://mlsanity.com/paper/2306.04018/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 65 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 30 of 40)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [GuacaMol: Benchmarking Models for De Novo Molecular Design](https://mlsanity.com/paper/1811.09621/) (2018)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [Policy Learning for Fairness in Ranking](https://mlsanity.com/paper/1902.04056/) (2019)
- [WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach](https://mlsanity.com/paper/2104.01767/) (2021)
- [Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space](https://mlsanity.com/paper/1909.11655/) (2019)
- [MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design](https://mlsanity.com/paper/2203.14500/) (2022)
- [Differentiable Scaffolding Tree for Molecular Optimization](https://mlsanity.com/paper/2109.10469/) (2021)
- [MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization](https://mlsanity.com/paper/2010.02318/) (2020)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [StageNet: Stage-Aware Neural Networks for Health Risk Prediction](https://mlsanity.com/paper/2001.10054/) (2020)
- [RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data](https://mlsanity.com/paper/1807.08820/) (2018)
- [Behavioral Use Licensing for Responsible AI](https://mlsanity.com/paper/2011.03116/) (2020)
- [TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery](https://mlsanity.com/paper/2202.08320/) (2022)
- [COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching](https://mlsanity.com/paper/2006.08765/) (2020)

## Built on by (most cited first, 3 of 3)

- [Accelerating Clinical Evidence Synthesis with Large Language Models](https://mlsanity.com/paper/2406.17755/) (2024)
- [MMCTOP: A Multimodal Textualization and Mixture-of-Experts Framework for Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2512.21897/) (2025)
- [DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginations](https://mlsanity.com/paper/2604.23054/) (2026)

## Abstract (arXiv)

Clinical trials are conducted to test the effectiveness and safety of potential drugs in humans for regulatory approval. Machine learning (ML) has recently emerged as a new tool to assist in clinical trials. Despite this progress, there have been few efforts to document and benchmark ML4Trial algorithms available to the ML research community. Additionally, the accessibility to clinical trial-related datasets is limited, and there is a lack of well-defined clinical tasks to facilitate the development of new algorithms. To fill this gap, we have developed PyTrial that provides benchmarks and open-source implementations of a series of ML algorithms for clinical trial design and operations. In this paper, we thoroughly investigate 34 ML algorithms for clinical trials across 6 different tasks, including patient outcome 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 their working 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 with just a few lines of code. Furthermore, our modular API architecture empowers practitioners to expand the framework to incorporate new algorithms and tasks effortlessly. The code is available at https://github.com/RyanWangZf/PyTrial.