TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction
arxiv(2024)
摘要
The clinical trial process, also known as drug development, is an
indispensable step toward the development of new treatments. The major
objective of interventional clinical trials is to assess the safety and
effectiveness of drug-based treatment in treating certain diseases in the human
body. However, clinical trials are lengthy, labor-intensive, and costly. The
duration of a clinical trial is a crucial factor that influences overall
expenses. Therefore, effective management of the timeline of a clinical trial
is essential for controlling the budget and maximizing the economic viability
of the 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
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