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KG-TREAT:通过将患者数据与知识图进行协同,进行治疗效果估计的预培训

Ruoqi Liu¹, Lingfei Wu², Ping Zhang¹

¹The Ohio State University.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence

|April 23, 2024

概括

这项研究引入了KG-TREAT,这是一个新的框架,通过将患者数据与知识图相结合来增强治疗效果估计 (TEE). KG-TREAT显著提高了TEE的准确性,超过了现有的方法.

科学领域:

生物医学信息学生物医学信息学
机器学习机器学习
因果推理因果推理

背景情况:

治疗效果估计 (TEE) 对个性化医疗至关重要,但受到有限的标记数据和复杂的高维患者数据的阻碍.
现有的TEE方法在稀少的观测数据中扎,这限制了它们在现实世界中的适用性.

研究的目的:

引入KG-TREAT,这是一个利用生物医学知识图 (KG) 和大规模观测数据来改进TEE的新型框架.
解决观察性患者数据集中的数据稀疏性和高维度挑战,以便更准确地评估治疗影响.

主要方法:

开发了KG-TREAT,这是一个预培训和微调框架,将患者数据与双重重点的生物医学KG整合在一起.
采用深度双层注意力协同效应的方法来融合治疗-共变量和结果-共变量关系.
整合了两个全面数据和KG上下文化的预培训任务.

主要成果:

在四个下游TEE任务中,KG-TREAT表现出卓越的性能.
在ROC曲线下的面积 (AUC) 中平均改善了7%,在估计异质效应的基于影响函数的精度 (IF-PEHE) 中平均改善了9%.
通过与随机临床试验的发现保持一致来验证有效性.

结论:

通过使用观察数据,KG-TREAT在治疗效果估计方面取得了重大进展.
该框架集成KG的能力提高了识别治疗影响的准确性和可靠性.
结果表明KG-TREAT有可能改善临床决策和个性化治疗策略.

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