HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data

TL;DR

HINT is a hierarchical interaction network that predicts clinical trial outcomes using multi-modal web data, knowledge embeddings, and a dynamic graph neural network, significantly improving prediction accuracy over existing methods.

Contribution

This paper introduces HINT, a novel hierarchical interaction network that integrates diverse web data and domain knowledge for accurate clinical trial outcome prediction.

Findings

HINT achieves high PR-AUC scores across different trial phases.

HINT outperforms baseline methods by up to 12.4% in PR-AUC.

The model effectively handles missing data and complex domain relations.

Abstract

Clinical trials are crucial for drug development but are time consuming, expensive, and often burdensome on patients. More importantly, clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. If we were better at predicting the results of clinical trials, we could avoid having to run trials that will inevitably fail more resources could be devoted to trials that are likely to succeed. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions for all diseases based on a comprehensive and diverse set of web data including molecule information of the drugs, target disease information, trial protocol and biomedical knowledge. HINT first encode these multi-modal data into latent embeddings, where an imputation module is designed to handle missing data. Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web. Then the interaction graph module will connect all the embedding via domain knowledge to fully capture various trial components and their complex relations as well as their influences on trial outcomes. Finally, HINT learns a dynamic attentive graph neural network to predict trial outcome. Comprehensive experimental results show that HINT achieves strong predictive performance, obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and indication outcome prediction, respectively. It also consistently outperforms the best baseline method by up to 12.4\% on PR-AUC.