心房颤动复发数据集
该数据集是一个包含1508名患者心房颤动发作记录的医疗健康数据集，数据模态主要为临床表格数据，通过整合结构化电子健康记录和自由文本出院报告生成，用于预测心房颤动的复发，支持评估传统临床评分、机器学习模型及大型表格模型的性能，并研究性别和年龄等因素对预测的影响。
基本信息
资源简介
心房颤动复发数据集
【数据集背景】
本数据集源站标题为《arXiv Query: search_query=&id_list=2505.14643v1&start=0&max_results=1》。源站首次发布：2025-05-20。
【数据内容】
源站原始描述：BACKGROUND: Atrial fibrillation (AF), the most common arrhythmia, is linked to high morbidity and mortality. In a fast-evolving AF rhythm control treatment era, predicting AF recurrence after its onset may be crucial to achieve the optimal therapeutic approach, yet traditional scores like CHADS2-VASc, HATCH, and APPLE show limited predictive accuracy. Moreover, early diagnosis studies often rely on codified electronic health record (EHR) data, which may contain errors and missing information. OB…
【主题与分类】
主题标签：cs.LG。
【获取与许可】
源站页面：https://arxiv.org/abs/2505.14643v1
下载信息
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使用方式
数据集说明
心房颤动复发数据集 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2505.14643v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:BACKGROUND: Atrial fibrillation (AF), the most common arrhythmia, is linked to high morbidity and mortality. In a fast-evolving AF rhythm control treatment era, predicting AF recurrence after its onset may be crucial to achieve the optimal therapeutic approach, yet traditional scores like CHADS2-VASc, HATCH, and APPLE show limited predictive accuracy. Moreover, early diagnosis studies often rely on codified electronic health record (EHR) data, which may contain errors and missing information.
OBJECTIVE: This study aims to predict AF recurrence between one month and two years after onset by evaluating traditional clinical scores, ML models, and our LTM approach. Moreover, another objective is to develop a methodology for integrating structured and unstructured data to enhance tabular dataset quality.
METHODS: A tabular dataset was generated by combining structured clinical data with free-text discharge reports processed through natural language processing techniques, reducing errors and annotation effort. A total of 1,508 patients with documented AF onset were identified, and models were evaluated on a manually annotated test set. The proposed approach includes a LTM compared against traditional clinical scores and ML models.
RESULTS: The proposed LTM approach achieved the highest predictive performance, surpassing both traditional clinical scores and ML models. Additionally, the gender and age bias analyses revealed demographic disparities.
CONCLUSION: The integration of structured data and free-text sources resulted in a high-quality dataset. The findings emphasiz
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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