GASCADE：面向增强癌症药物警戒的药品不良事件分组摘要
GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance
AI总结：
本文提出GASCADE框架，结合大语言模型信息抽取与T5摘要能力，并首次将Direct Preference Optimization等对齐技术用于编码器-解码器摘要模型，同时构建MCADRS数据集，以提升癌症药物警戒中药品不良事件分组摘要的性能。
摘要：
在癌症治疗领域，对患者使用处方药后报告的药品不良事件（ADEs）进行摘要，对于加强药物警戒实践和改善药物相关决策至关重要。随着药物警戒数据的数量和复杂性不断增加，该领域现有研究主要集中于一般性疾病，而非专门针对癌症。本文引入了对多名使用同一药物治疗癌症的患者所报告的药品不良事件进行分组摘要的任务。为应对癌症药物警戒中资源有限的挑战，我们提出了多标签癌症药物不良反应与摘要（MCADRS）数据集。该数据集包含药物警戒帖子，详细描述了患者对药物疗效和不良反应的关切，同时包含提取出的药物名称、药品不良事件、严重程度和反应不利性标签，以及每种药物的ADEs摘要。此外，我们提出了癌症药品不良事件分组与抽象式摘要（GASCADE）框架，这是一个新颖的流程，将大语言模型（LLMs）的信息抽取能力与编码器-解码器T5模型的摘要能力相结合。我们的工作是首次将对齐技术（包括Direct Preference Optimization等先进算法）应用于使用合成数据集进行摘要任务的编码器-解码器模型。通过大量实验，我们证明了GASCADE在多种指标上的优越性能，并通过自动评估和人工评估进行了验证。这种多任务方法增强了药物相关决策，促进了对患者关切的更深入理解，为个性化和响应式癌症护理的进步铺平了道路。本文使用的代码和数据集已公开提供。
英文摘要：
In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharmacovigilance practices and improving drug-related decision-making. While the volume and complexity of pharmacovigilance data have increased, existing research in this field has predominantly focused on general diseases rather than specifically addressing cancer. This work introduces the task of grouped summarization of adverse drug events reported by multiple patients using the same drug for cancer treatment. To address the challenge of limited resources in cancer pharmacovigilance, we present the MultiLabeled Cancer Adverse Drug Reaction and Summarization (MCADRS) dataset. This dataset includes pharmacovigilance posts detailing patient concerns regarding drug efficacy and adverse effects, along with extracted labels for drug names, adverse drug events, severity, and adversity of reactions, as well as summaries of ADEs for each drug. Additionally, we propose the Grouping and Abstractive Summarization of Cancer Adverse Drug events (GASCADE) framework, a novel pipeline that combines the information extraction capabilities of Large Language Models (LLMs) with the summarization power of the encoder-decoder T5 model. Our work is the first to apply alignment techniques, including advanced algorithms like Direct Preference Optimization, to encoder-decoder models using synthetic datasets for summarization tasks. Through extensive experiments, we demonstrate the superior performance of GASCADE across various metrics, validated through both automated assessments and human evaluations. This multitasking approach enhances drug-related decision-making and fosters a deeper understanding of patient concerns, paving the way for advancements in personalized and responsive cancer care. The code and dataset used in this work are publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
