TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models

TL;DR

This paper introduces a novel deep & cross network combined with large language models to predict clinical trial enrollment success, leveraging semantic features from eligibility criteria for improved accuracy and interpretability.

Contribution

It presents a new model that integrates LLM-augmented text features with deep & cross networks, enhancing prediction accuracy and interpretability in clinical trial recruitment forecasting.

Findings

Achieved PR-AUC of 0.7002, outperforming existing methods.

Demonstrated interpretability by identifying influential eligibility criteria sentences.

Provided publicly available code and dataset for reproducibility.

Abstract

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.