Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates

TL;DR

This paper introduces a deep learning model that uses pre-trained language models and probabilistic layers to accurately predict clinical trial enrollment and duration, including uncertainty estimates, based on real-world data.

Contribution

It presents a novel neural network approach combining PLMs and Gamma distribution-based uncertainty modeling for clinical trial enrollment prediction.

Findings

Outperforms baseline models in enrollment prediction accuracy

Effectively captures uncertainty in predictions

Successfully predicts site-level enrollment and trial duration

Abstract

Clinical trials are a systematic endeavor to assess the safety and efficacy of new drugs or treatments. Conducting such trials typically demands significant financial investment and meticulous planning, highlighting the need for accurate predictions of trial outcomes. Accurately predicting patient enrollment, a key factor in trial success, is one of the primary challenges during the planning phase. In this work, we propose a novel deep learning-based method to address this critical challenge. Our method, implemented as a neural network model, leverages pre-trained language models (PLMs) to capture the complexities and nuances of clinical documents, transforming them into expressive representations. These representations are then combined with encoded tabular features via an attention mechanism. To account for uncertainties in enrollment prediction, we enhance the model with a probabilistic layer based on the Gamma distribution, which enables range estimation. We apply the proposed model to predict clinical trial duration, assuming site-level enrollment follows a Poisson-Gamma process. We carry out extensive experiments on real-world clinical trial data, and show that the proposed method can effectively predict the number of patients enrolled at a number of sites for a given clinical trial, outperforming established baseline models.