# TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction

arXiv: https://arxiv.org/abs/2404.13235 · HTML: https://mlsanity.com/paper/2404.13235/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model](https://mlsanity.com/paper/2404.01273/) (2024)
- [Uncertainty Quantification on Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2401.03482/) (2024)
- [Deep Learning based Multi-Label Image Classification of Protest Activities](https://mlsanity.com/paper/2301.04212/) (2023)

## Built on by (most cited first, 7 of 7)

- [ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning](https://mlsanity.com/paper/2404.14777/) (2024)
- [SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction](https://mlsanity.com/paper/2408.05696/) (2024)
- [DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing](https://mlsanity.com/paper/2407.02265/) (2024)
- [Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation](https://mlsanity.com/paper/2410.12476/) (2024)
- [TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models](https://mlsanity.com/paper/2407.13115/) (2024)
- [A Survey on LLM-Assisted Clinical Trial Recruitment](https://mlsanity.com/paper/2506.15301/) (2025)
- [Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates](https://mlsanity.com/paper/2507.23607/) (2025)

## Abstract (arXiv)

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.