Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization

TL;DR

This paper presents a novel approach that leverages multi-document summarization of PubMed abstracts to predict the effectiveness of interventions in clinical trials, aiming to reduce uncertainty and improve decision-making.

Contribution

The study introduces a new method combining multi-document summarization and BERT-based classification to predict clinical trial outcomes, along with a novel dataset for evaluation.

Findings

Summaries improve prediction accuracy of intervention effectiveness.

The BERT-based classifier outperforms baseline models.

Effective summaries correlate strongly with trial outcomes.

Abstract

Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this study, we propose a new method to predict the effectiveness of an intervention in a clinical trial. Our method relies on generating an informative summary from multiple documents available in the literature about the intervention under study. Specifically, our method first gathers all the abstracts of PubMed articles related to the intervention. Then, an evidence sentence, which conveys information about the effectiveness of the intervention, is extracted automatically from each abstract. Based on the set of evidence sentences extracted from the abstracts, a short summary about the intervention is constructed. Finally, the produced summaries are used to train a BERT-based classifier, in order to infer the effectiveness of an intervention. To evaluate our proposed method, we introduce a new dataset which is a collection of clinical trials together with their associated PubMed articles. Our experiments, demonstrate the effectiveness of producing short informative summaries and using them to predict the effectiveness of an intervention.