TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction

Ling Yue, Sixue Xing, Jonathan Li, Md Zabirul Islam, Bolun Xia, Jintai Chen, Tianfan Fu

Abstract: The clinical trial process is crucial for ensuring the safety and efficacy of new pharmaceuticals but is often hindered by lengthy durations, high costs, and labor-intensive processes. These challenges delay the availability of life-saving treatments and increase financial burdens on pharmaceutical companies, with average costs exceeding 2 billion dollars per trial. Efficient management of trial timelines is essential to control budgets and improve the economic feasibility of research.To address these challenges, we introduce TrialDura, an innovative machine learning-based framework designed to predict the duration of clinical trials. TrialDura utilizes a multimodal approach, incorporating diverse data sources such as disease names, drug molecules, trial phases, and detailed eligibility criteria. These elements are encoded using Bio-BERT embeddings, which are specifically tuned for biomedical contexts to provide a comprehensive semantic understanding of the trial data. The model employs a hierarchical attention mechanism that effectively integrates these embeddings, capturing complex interactions between different data modalities to enhance prediction accuracy.Our extensive experiments demonstrate that TrialDura significantly outperforms existing models, achieving a mean absolute error (MAE) of 1.04 years and a root mean square error (RMSE) of 1.39 years. This performance underscores the model's ability to deliver more precise predictions of trial durations, which can facilitate better planning and resource allocation in clinical research. By providing insights into expected trial timelines, TrialDura aids in optimizing trial management strategies, potentially reducing costs and accelerating the development of new treatments.Furthermore, TrialDura's interpretability, enabled by its hierarchical attention mechanism, offers valuable insights into the factors influencing trial durations, assisting clinicians and researchers in making informed decisions. The model's ability to highlight key features contributing to duration predictions enhances its utility as a decision-support tool in the pharmaceutical industry.In addition to its predictive capabilities, TrialDura marks a significant advancement in applying artificial intelligence to clinical trials. By leveraging state-of-the-art natural language processing techniques and integrating them with structured clinical data, TrialDura not only enhances the accuracy of duration predictions but also deepens the understanding of factors driving trial timelines. This comprehensive approach allows for a more nuanced analysis of clinical trial data, providing stakeholders with actionable insights to inform strategic decision-making and improve the overall efficiency of the drug development process.The implications of TrialDura extend beyond individual trial predictions, offering a framework that can be adapted and scaled to address various challenges in clinical research. As the pharmaceutical industry continues to evolve, the ability to accurately predict and manage trial durations will become increasingly important, making tools like TrialDura indispensable for researchers and decision-makers. By fostering a deeper understanding of trial dynamics and enabling more effective resource allocation, TrialDura has the potential to transform the landscape of clinical trials, ultimately leading to faster and more cost-effective development of new therapies. The code for TrialDura is publicly available at https://github.com/LeoYML/TrialDura.

External IDs:doi:10.1145/3698587.3701434