Multilevel graphical modeling of heterogeneous healthcare data in a federated learning setting

希望彻底改变我们改善健康和治疗疾病的方式，目标是“在正确的时间，在正确的时间，向正确的人提供正确的治疗”--这是一个被称为“精确医学”的概念--几年来一直受到许多政治领导人和科学家的欢迎。随着现代医学的进步，大量的医疗接触(如就诊、检查、药物、成像、分子检测等)都发生在我们的医疗系统中。因此，为了更好的精准医学，医生现在必须用不现实的变量数量做出越来越复杂的治疗决定。这就是为什么人工智能(AI)的发展被设想为在医学上创造一场数据科学革命。特别是，图形神经网络(GNN)通过将图形模型的关系推理与深度学习的能力相结合，在学习有意义和强大的数据表示方面显示出巨大的潜力。然而，鉴于深度学习的力量与数据大小密切相关，并且由于患者隐私原因，医疗数据不能在医疗机构之间轻松共享，开发功能强大的GNN模型用于医疗保健中的疾病预测是一个重大挑战。这项研究计划的主要目标是开发一种方法框架，能够在联邦学习环境中对全谱健康数据进行综合建模，这将是医学人工智能进步的重要一步。首先，短期目标是为精确医学提出基于医学图像的分析方法，并确定何时更复杂的方法更适合不同的医学成像问题。第二个短期目标是通过医学文本笔记为疾病预测任务开发语言模型。第三个中期目标是开发图形结构，以便将医学中的不同数据组合在一起。最后，最后一个长期目标是将先前目标的所有发展整合到一个保护患者隐私的联合学习环境中。在这个联合学习框架内，可以从多个医疗保健机构的数据库开发GNN模型，从而增加正在分析的数据的大小。此外，数据始终保存在每个医疗机构的范围内，从而避免数据传输。通过推进和结合医学图像和文本分析、GNN和联合学习领域的知识，该研究计划建议改变科学界进行精确医学研究的方式。最终，这将导致人工智能技术在医学上更快的临床翻译和应用。

The hope of revolutionizing how we improve health and treat diseases, with the goal to "deliver the right treatment at the right time, every time, to the right person" - a concept known as "Precision Medicine" - has been embraced by many political leaders and scientists since several years. With the progress of modern medicine, a large number of medical encounters (e.g. medical visits, exams, medications, imaging, molecular testing, etc.) are taking place in our healthcare system. For better precision medicine, therefore, physicians must now make increasingly complex treatment decisions with an unrealistic number of variables. This is why artificial intelligence (AI) developments are envisioned to create a data science revolution in medicine. In particular, graphical neural networks (GNNs) have shown immense potential in learning meaningful and powerful data representations by combining relational inference of graphical models with the power of deep learning. However, given that the power of deep learning is strongly associated with data size and that medical data cannot be easily shared between medical institutions due to patient privacy reasons, developing powerful GNN models for disease prediction in healthcare is a major challenge. The main goal of this research program is to develop a methodological framework enabling the integrative modeling of the full spectrum of health data in a federated learning setting, which will be an important step for the progress of AI in medicine. A first, short-term objective is to propose medical image-based analysis methods for precision medicine and determining when more complex methods are better suited for different medical imaging problems. A second, short-term objective is to develop language models for disease prediction tasks via medical text notes. A third, mid-term objective is to develop the graphical structures allowing to combine heterogeneous data in medicine. Finally, a last, long-term objective is to integrate all developments of the previous objectives into a federated learning setting preserving patient privacy. Within this federated learning framework, GNN models can be developed from the databases of multiple healthcare institutions, thereby augmenting the size of the data being analyzed. Also, data is always kept within the confines of each healthcare institution, thereby avoiding data transfer. By advancing and combining knowledge in the fields of medical image and text analysis, GNNs and federated learning, this research program proposes to change how precision medicine research is conducted by the scientific community. Ultimately, this will lead to a faster clinical translation and utilization of AI techniques in medicine.