使用基于语义的诱导推断和知识图嵌入的临床试验建议
Murthy V Devarakonda1, Smita Mohanty1, Raja Rao Sunkishala1
1Biomedical Research, Novartis, Cambridge, MA, USA.
Journal of biomedical informatics
|April 1, 2024
概括
这项研究引入了一种使用知识图嵌入和诱导推断来推临床试验设计的新方法. 该方法有效地挖掘了过去的试验数据,改善了未来的临床试验规划.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床试验设计 临床试验设计
- 人工智能的人工智能
背景情况:
- 临床试验设计需要许多复杂的决策.
- 挖掘历史临床试验数据可以为这些决定提供信息.
- 现有的方法缺乏基于数据的全面建议.
研究的目的:
- 开发一个临床试验设计的推系统.
- 为此目的,利用知识图嵌入和归纳推理.
- 提高临床试验规划的效率和有效性.
主要方法:
- 从临床试验数据构建了一个新的知识图.
- 应用神经嵌入,并评估各种嵌入技术.
- 使用语义驱动的诱导推理方法进行推.
- 使用来自clinicaltrials.gov.gov的公开数据.
主要成果:
- 建议的相关性得分在70%至83%之间.
- 证明了排名最高的建议是非常相关的.
- 验证了拟议的知识图和推理方法的有效性.
结论:
- 使用节点语义的诱导推断对于生成临床试验设计建议是有效的.
- 知识图嵌入为挖掘临床试验数据提供了一种强大的方法.
- 有潜力进一步增强使用节点语义的图形嵌入培训.
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