MCADRS（多标签癌症药物不良反应与摘要数据集）
MCADRS数据集包含2000条癌症药物不良反应帖子，提供药物名称、不良反应标签及摘要，用于癌症药物警戒与摘要生成。
基本信息
资源简介
MCADRS数据集由印度理工学院帕特纳分校创建，用于癌症药物警戒。包含2000条关于癌症药物不良反应的帖子，涵盖791种药物，提供药物名称、不良反应、严重性等标签以及每个药物的不良反应摘要，支持药物不良反应分组摘要任务。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
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使用方式
数据集获取
git clone https://github.com/SofeeyaJ/GASCADE_ECIR2025.git
curl -L -o repo.zip https://github.com/SofeeyaJ/GASCADE_ECIR2025/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
This repository hosts the code and datasets associated with our paper, “GASCADE: Grouped Summarization of Adverse Drug Events for Enhanced Cancer Pharmacovigilance.”
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 are made available here.
Any resources, ideas, or methodologies derived from our work should be properly cited using the following reference:
Jamil, S., Dabad, A., Reddy, B.A., Saha, S., Misra, R., Shakur, A.A. (2025). GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance. In: Hauff, C., et al. Advances in Information Re
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
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