TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction
<|reference_start|>TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction: 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.<|reference_end|>
@article{yue2024trialdura:,
    title={TrialDura: Hierarchical Attention Transformer for Interpretable Clinical
  Trial Duration Prediction},
    author={Ling Yue, Jonathan Li, Sixue Xing, Md Zabirul Islam, Bolun Xia,
  Tianfan Fu, Jintai Chen},
    journal={arXiv preprint arXiv:2404.13235},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13235},
    primaryClass={cs.LG}
}