A two-stage framework by leveraging large language model for predicting clinical trial outcomes

Abstract

Clinical trials are essential for discovering new treatments and advancing medical knowledge. However, the high uncertainty of carrying out clinical trials often ends with ineffective results. Therefore, the accurate prediction of clinical trial outcomes has become a significant challenge. Numerous publicly accessible clinical trial reports have been discovered to be beneficial in alleviating this challenge but lack necessary annotations to be formal datasets for deep model training. To address the issue, this paper proposes to construct a new clinical trial dataset by extracting publicly available clinical trial reports from ClinicalTrials.gov and PubMed. In addition, a new two-stage method is proposed for the prediction of clinical trial outcomes across all trial phases. Specifically, our method first employs a prompt template combined with each clinical trial report to prompt a large language model to generate a concise summarization text containing essential information related to the clinical trial outcomes. Subsequently, this summarization text is utilized to train a classifier to predict the outcomes. Extensive experiments were conducted on the dataset, and our method was compared with several state-of-the-art classification models. The results showed that our method achieved the best performance in predicting clinical trial outcomes, especially using small amounts of training data under a data imbalance difficulty.