PolyMed: A Medical Dataset Addressing Disease Imbalance for Robust Automatic Diagnosis Systems

We introduce the PolyMed dataset, designed to address the limitations of existing medical case data for Automatic Diagnosis Systems (ADS). ADS assists doctors by predicting diseases based on patients' basic information, such as age, gender, and symptoms. However, these systems face challenges due to imbalanced disease label data and difficulties in accessing or collecting medical data. To tackle these issues, the PolyMed dataset has been developed to improve the evaluation of ADS by incorporating medical knowledge graph data and diagnosis case data. The dataset aims to provide comprehensive evaluation, include diverse disease information, effectively utilize external knowledge, and perform tasks closer to real-world scenarios. We have also made the data collection tools publicly available to enable researchers and other interested parties to contribute additional data in a standardized format. These tools feature a range of customizable input fields that can be selectively utilized according to the user's specific requirements, ensuring consistency and professionalism in the data collection process. All train and test code of our data available in https://github.com/krchanyang/PolyMed本研究提出PolyMed数据集，旨在弥补现有面向自动诊断系统（Automatic Diagnosis Systems，ADS）的医疗病例数据存在的局限。自动诊断系统可辅助医师基于患者的年龄、性别、症状等基础信息预测疾病。然而此类系统面临两大核心挑战：一是疾病标签数据分布失衡，二是医疗数据的获取与采集难度较高。为解决上述问题，本研究构建PolyMed数据集，通过融合医疗知识图谱数据与诊断病例数据，以优化自动诊断系统的评估范式。该数据集的设计目标包括：提供全面的评估支持、覆盖多样化的疾病信息、有效利用外部知识，以及构建更贴近真实临床场景的任务环境。本研究同时公开了配套的数据采集工具，以便研究人员及其他相关方能够以标准化格式补充提交新增数据。该工具集内置一系列可自定义的输入字段，用户可根据具体需求选择性启用，以此保障数据采集过程的规范性与专业性。本数据集配套的全部训练与测试代码已公开于：https://github.com/krchanyang/PolyMed