医疗健康大数据驱动的人工智能辅助罕见病（HAE）筛查模型

利用贵州省丰富的大数据资源、深厚的医学知识积累以及先进的人工智能技术，构建并验证针对罕见病单病种（HAE-遗传性血管性水肿）的高危筛查模型。通过应用数据治理技术对筛查模型中的关键字段进行精细化管理与优化，以确保数据的准确性和可靠性。最终，筛选出的高危疑似罕见病（HAE）患者信息将被及时反馈给试点合作的罕见病诊疗中心，以便为这些患者提供进一步的专业诊疗服务。此项目的实施有望显著提升罕见病（HAE）的早期发现与诊疗效率，为贵州省乃至更广泛地区的罕见病患者带来福音。

Leveraging Guizhou Province's abundant big data resources, rich accumulated medical expertise, and state-of-the-art artificial intelligence technologies, this study developed and validated a high-risk screening model for a single rare disease, HAE (hereditary angioedema). Data governance technologies were applied to conduct refined management and optimization of the key fields in the screening model, ensuring the accuracy and reliability of the data. Ultimately, the information of the screened high-risk suspected HAE patients will be timely fed back to the pilot cooperative rare disease diagnosis and treatment centers to provide further professional diagnosis and treatment services for these patients. The implementation of this project is expected to significantly improve the early detection and treatment efficiency of HAE, bringing tangible benefits to rare disease patients in Guizhou Province and even broader regions.

搜集汇总

数据集介绍

特点

该数据集基于贵州省二级及以上公立医院的HAE患者数据，涵盖2019年至2024年的患者信息，包括年龄、性别、既往史、检验检查、用药等。通过数据治理技术进行标准化处理，确保数据的准确性和可靠性，适用于HAE疾病的医药研究和诊疗效率提升。