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对改善移植结果的AI的NephroCAGE-德国-加拿大联盟:算法开发和验证研究协议
Matthieu-P Schapranow1, Mozhgan Bayat1, Aadil Rasheed1
1Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
JMIR research protocols
|December 22, 2023
概括
这项研究使用人工智能 (AI) 和联合学习来利用跨国移植数据预测移植后的风险,通过保护隐私的数据分析增强患者的结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 人工智能算法越来越多地用于各种领域的复杂数据分析.
- 这项研究探讨了人工智能在移植科中的应用,用于预测严重的移植后事件.
- 它涉及结合跨国真实世界移植数据,需要强有力的法律和技术保障.
研究的目的:
- 开发和评估过程,软件和方法,以结合来自德国和加拿大的移植数据 (超过8000例).
- 实施保护敏感移植数据的措施.
- 使用跨国数据建立高质量的预测AI模型的基础.
主要方法:
- 将在私有区块链上实施一个去中心化的NephroCAGE联合学习基础设施.
- 临床预测模型 (CPM) 将转移到临床场所进行本地分析,保护敏感数据.
- 人工智能算法 (例如随机森林,极端梯度提升) 将与CPM开发进行比较,以预测移植失败或死亡率.
主要成果:
- 在2021年2月获得初始资金.
- 到2022年,从所有临床合作伙伴获得伦理审批.
- 从临床移植数据库中提取变量从2022年开始;到2023年8月检索了8120个患者记录.
- 目前正在进行CPM的开发和验证.
结论:
- 这项研究开创了德国和加拿大移植数据的结合.
- 联合学习将被用于对现实世界移植数据的CPM的隐私保护培训.
- 将开发一个学习软件系统来分析人口特异性和个人对移植后结果的影响.
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