  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.
