展望回顾:在未来对分布式数据网络的分析中,生成性人工智能是否会使常见的数据模型过时?
Jeffery L Painter1, Darmendra Ramcharran2, Andrew Bate3,4
1GSK, 410 Blackwell Street, Durham, NC 27701, USA.
Therapeutic advances in drug safety
|April 28, 2025
概括
生成型人工智能 (GenAI) 和知识图 (KG) 可能取代传统的共同数据模型 (CDM) 进行医疗数据分析. 这种方法可以直接查询原始数据,克服CDM的局限性并增强实时洞察力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 整合现实世界医疗保健数据是复杂的,因为不同的格式和术语,需要资源密集型标准化.
- 共同数据模型 (CDM) 提高了互操作性,但可能导致信息丢失,语义不一致以及高实施/更新成本.
研究的目的:
- 探索生成人工智能 (GenAI),特别是大型语言模型 (LLM) 如何在定量医疗数据分析中克服CDM的局限性.
- 提出第四代分布式数据网络分析,利用GenAI和知识图 (KG).
主要方法:
- 审查GenAI (LLM) 解释自然语言查询和生成与原始医疗数据直接交互的代码的潜力.
- 整合知识图 (KG) 以在异质数据中标准化语义关系,保持数据完整性.
- 提出第四代分布式数据网络分析的框架.
主要成果:
- 通过通过自然语言查询和自动代码生成,GenAI可能会使CDM过时,从而使原始数据的直接分析成为可能.
- 基因基因可以标准化语义和关系,保持数据完整性并实现有效的GenAI.
- 基于GenAI的方法与KG提供了跨多种数据集的高效实时分析的潜力.
结论:
- 结合KG,GenAI为CDM提供了一个有希望的替代方案,用于定量医疗数据分析,提高效率和数据完整性.
- 建议进行进一步的研究,以评估GenAI在医疗数据分析中的变革潜力,确保隐私,安全和治理.
- 这种方法旨在克服目前数据标准化和分析方面的局限性,最终提高患者安全.
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