Coulter Department of Biomedical Engineering, Georgia Insitute of Technology, Atlanta, USA.
Department of Electrical and Computer Engineering, Georgia Insitute of Technology, Atlanta, USA.
BMC Med Inform Decis Mak. 2024 May 27;24(1):137. doi: 10.1186/s12911-024-02510-6.
Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient.
In this study, we present a new algorithmic framework using meta-learning for learning personalized causal graphs in biomedicine. Our framework extracts common patterns from multiple patient graphs and applies this information to develop individualized graphs. In multi-task causal graph learning, the proposed optimized initial guess of shared commonality enables the rapid adoption of knowledge to new tasks for efficient causal graph learning.
Experiments on one real-world biomedical causal graph learning benchmark data and four synthetic benchmarks show that our algorithm outperformed the baseline methods. Our algorithm can better understand the underlying patterns in the data, leading to more accurate predictions of the causal graph. Specifically, we reduce the structural hamming distance by 50-75%, indicating an improvement in graph prediction accuracy. Additionally, the false discovery rate is decreased by 20-30%, demonstrating that our algorithm made fewer incorrect predictions compared to the baseline algorithms.
To the best of our knowledge, this is the first study to demonstrate the effectiveness of meta-learning in personalized causal graph learning and cause inference modeling for biomedicine. In addition, the proposed algorithm can also be generalized to transnational research areas where integrated analysis is necessary for various distributions of datasets, including different clinical institutions.
通过图进行因果关系建模，即因果图学习，为因果关系的动态提供了一种恰当的描述。大多数临床决策支持系统中的现有机器学习模型仅预测变量之间的关联，而因果图学习模型则通过图来描述因果关系动态。然而，由于每个患者的数据有限，为每个人构建个性化的因果图具有挑战性。
在这项研究中，我们提出了一种新的算法框架，用于在生物医学中学习个性化因果图的元学习。我们的框架从多个患者图中提取共同模式，并将此信息应用于开发个性化图。在多任务因果图学习中，所提出的共享共性的优化初始猜测可使新任务快速采用知识，从而实现高效的因果图学习。
在一个真实的生物医学因果图学习基准数据集和四个合成基准数据集上的实验表明，我们的算法优于基线方法。我们的算法可以更好地理解数据中的潜在模式，从而更准确地预测因果图。具体来说，我们将结构汉明距离降低了 50-75%，表明图预测准确性有所提高。此外，假发现率降低了 20-30%，表明与基线算法相比，我们的算法做出的错误预测更少。
据我们所知，这是第一项证明元学习在生物医学个性化因果图学习和因果推理建模中的有效性的研究。此外，所提出的算法还可以推广到跨国研究领域，在这些领域中，对于包括不同临床机构在内的各种数据集分布，集成分析是必要的。
