GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance
Journal:
arXiv
Published Date:
May 7, 2025
Abstract
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.
