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

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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.