TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction

TL;DR

TrialDura introduces a hierarchical attention transformer model that leverages multimodal biomedical data and Bio-BERT embeddings to accurately predict clinical trial durations, aiding in efficient trial management.

Contribution

The paper presents a novel hierarchical attention transformer model that effectively encodes multimodal clinical trial data for duration prediction, outperforming existing models.

Findings

Achieved MAE of 1.04 years in duration prediction

Outperformed baseline models in accuracy

Demonstrated effectiveness of Bio-BERT embeddings in biomedical context

Abstract

The clinical trial process, a critical phase in drug development, is essential for developing new treatments. The primary goal of interventional clinical trials is to evaluate the safety and efficacy of drug-based treatments for specific diseases. However, these trials are often lengthy, labor-intensive, and expensive. The duration of a clinical trial significantly impacts overall costs, making efficient timeline management crucial for controlling budgets and ensuring the economic feasibility of research. To address this issue, We propose TrialDura, a machine learning-based method that estimates the duration of clinical trials using multimodal data, including disease names, drug molecules, trial phases, and eligibility criteria. Then, we encode them into Bio-BERT embeddings specifically tuned for biomedical contexts to provide a deeper and more relevant semantic understanding of clinical trial data. Finally, the model's hierarchical attention mechanism connects all of the embeddings to capture their interactions and predict clinical trial duration. Our proposed model demonstrated superior performance with a mean absolute error (MAE) of 1.04 years and a root mean square error (RMSE) of 1.39 years compared to the other models, indicating more accurate clinical trial duration prediction. Publicly available code can be found at: https://anonymous.4open.science/r/TrialDura-F196.