Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization

Georgios Katsimpras, Georgios Paliouras


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.
Anthology ID:
2022.acl-long.137
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1947–1957
Language:
URL:
https://aclanthology.org/2022.acl-long.137
DOI:
10.18653/v1/2022.acl-long.137
Bibkey:
Cite (ACL):
Georgios Katsimpras and Georgios Paliouras. 2022. Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1947–1957, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization (Katsimpras & Paliouras, ACL 2022)
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PDF:
https://aclanthology.org/2022.acl-long.137.pdf