TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
This work addresses the resource-intensive challenge of clinical trial recruitment for researchers and organizations, though it is incremental as it builds on existing deep learning and LLM techniques.
The paper tackled the problem of predicting clinical trial enrollment success by developing a deep & cross network with LLM-augmented text features, achieving a PR-AUC of 0.7002, which outperformed established methods.
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.