  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.
