TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
<|reference_start|>TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models: Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the statistical power of the treatment (e.g., a new drug) in curing a certain disease. Clinical trial recruitment has a significant impact on trial success. Forecasting whether the recruitment process would be successful before we run the trial would save many resources and time. This paper develops a novel deep & cross network with large language model (LLM)-augmented text feature that learns semantic information from trial eligibility criteria and predicts enrollment success. The proposed method enables interpretability by understanding which sentence/word in eligibility criteria contributes heavily to prediction. We also demonstrate the empirical superiority of the proposed method (0.7002 PR-AUC) over a bunch of well-established machine learning methods. The code and curated dataset are publicly available at https://anonymous.4open.science/r/TrialEnroll-7E12.<|reference_end|>
@article{yue2024trialenroll:,
    title={TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep &
  Cross Network and Large Language Models},
    author={Ling Yue, Sixue Xing, Jintai Chen, Tianfan Fu},
    journal={arXiv preprint arXiv:2407.13115},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13115},
    primaryClass={cs.LG cs.CL}
}