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

联邦学习环境中异构医疗数据的多级图形建模

• 批准号: RGPIN-2021-03996
• 负责人: Vallières, Martin
• 金额: $ 1.75万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757318
• 关键词: Multilevel; graphical; modeling; heterogeneous; healthcare

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.

多年来，许多政治领导人和科学家都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，每次向正确的人提供正确的治疗”--这一概念被称为“精准医学”。随着现代医学的进步，大量的医疗接触（例如，医疗访问，检查，药物治疗，成像，分子测试等）发生在我们的医疗系统中。因此，为了更好的精准医疗，医生现在必须在不切实际的变量数量下做出越来越复杂的治疗决策。这就是为什么人工智能（AI）的发展被设想为在医学领域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表现出巨大的潜力。然而，鉴于深度学习的能力与数据大小密切相关，并且由于患者隐私原因，医疗数据无法在医疗机构之间轻松共享，因此开发用于医疗保健疾病预测的强大GNN模型是一个重大挑战。 该研究计划的主要目标是开发一种方法框架，以便在联邦学习环境中对全方位健康数据进行综合建模，这将是人工智能在医学领域取得进展的重要一步。第一个短期目标是提出用于精确医学的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注释开发用于疾病预测任务的语言模型。第三，中期目标是开发图形结构，允许联合收割机在医学异构数据。最后，最后一个长期目标是将先前目标的所有发展整合到保护患者隐私的联邦学习环境中。在这个联合学习框架内，GNN模型可以从多个医疗机构的数据库中开发，从而增加了所分析数据的大小。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和联邦学习领域的知识，该研究计划提出改变科学界进行精准医学研究的方式。最终，这将导致AI技术在医学中更快的临床转化和利用。