欧盟项目Real4Reg:利用人工智能解锁真实世界的数据
Jonas Peltner1, Cornelia Becker2, Julia Wicherski2
1German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Health research policy and systems
|February 27, 2025
概括
Real4Reg项目通过开发人工智能驱动的解决方案来分析真实世界数据 (RWD) 来增强监管决策. 该倡议旨在改善整个产品生命周期的药物安全性和有效性评估.
科学领域:
- 监管科学 监管科学
- 卫生技术评估 卫生技术评估
- 数据科学数据科学数据科学
背景情况:
- 现实世界数据 (RWD) 的使用是在授权后的监管过程中建立的,但在授权前的阶段面临挑战.
- RWD的有限可用性,异质性和分析复杂性阻碍了其监管应用.
- 医疗保健研究中的新兴人工智能提供了机会,但需要了解如何将其应用于各种RWD,用于监管决策和健康技术评估 (HTA).
研究的目的:
- 通过为欧盟监管机构和HTA机构开发用户友好的分析解决方案,使RWD的利用成为可能.
- 在监管决策和HTA中实施人工智能算法,以进行有效的RWD分析.
- 调查各种RWD来源在产品生命周期中产生高质量,基于人口的信息的价值.
主要方法:
- "Real4Reg"项目使用了四个用例来展示RWD分析的最佳实践.
- 这些用例涵盖了评估,事先授权,观察数据的外部有效性,授权后的安全研究和比较有效性.
- 该项目介绍了其目标,结构和在欧洲RWD景观中的作用.
主要成果:
- Real4Reg确定并描述了新的/优化的RWD分析方法的好处和风险.
- 评估的关键方面包括安全性,有效性,互操作性,适当性,可访问性,价值创造和可持续性.
- 该项目旨在提供支持更好的药物决策的证据.
结论:
- "Real4Reg"项目的研究结果将有助于改善关于药物的决策.
- 改进的RWD分析方法最终将有利于患者的健康.
- 现实4Reg注册在HMA-EMA现实世界数据源和研究目录 (EU PAS号 EUPAS105544) 中.
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