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为什么骨关节炎没有治疗方法 - 基于人工智能的大数据分析为推进现场领域提供了机会
F Saxer1, G Jansen2, S M A Bierma-Zeinstra3
1Novartis Biomedical Research, Novartis Campus, 4002 Basel, Switzerland; Medical Faculty, University of Basel, 4002 Basel, Switzerland.
Osteoarthritis and cartilage
|December 28, 2025
概括
机器学习 (ML) 在一个联合框架中可以分析多样化的骨关节炎 (OA) 数据,以改善患者细分,并彻底改变临床试验设计,以获得更好的患者结果.
科学领域:
- 利用先进的机器学习 (ML) 和联合学习 (FL) 进行医疗数据分析.
- 应用计算方法来理解复杂疾病的异质性.
背景情况:
- 由于患者异质性,骨关节炎 (OA) 研究没有产生新的治疗方法.
- 现有的OA患者数据是分散在各种来源的.
研究的目的:
- 利用联合机器学习来分析各种开放式数据集.
- 改进患者细分,疾病进展见解和代用终点开发.
- 通过协作努力彻底改变OA临床试验设计.
主要方法:
- 在本地数据集上实施保护隐私的联合训练算法.
- 将新的ML技术应用于多维医疗数据 (注册表,现实世界证据,试验数据).
- 促进专业间,机构间以及公私合作.
主要成果:
- 增强检测患者细分模式和预后洞察力的能力.
- 克服不完整性,结构异质性和多维性等数据挑战.
- 该基金会致力于开发共变量,数字双胞胎,合成对照组和试验模拟.
结论:
- 联合的ML框架可以释放丰富的OA数据和多样化的专业知识的潜力.
- 这种方法可以彻底改变OA试验设计,解决未满足的患者需求.
- 能够开发新的代用终点,并为丰富策略提供信息.
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