In diesem Semester wird die im WiSe 2022/23 erfolgreich gestartete Vortragsreihe Medical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Donnerstag ab 16:00 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.

The progression of diseases is a dynamic process, with clinical data providing insights into the trajectories of individual patients. However, individual patient data are often sparse, making it challenging to distinguish between natural variations and detrimental changes. Integrating data from different individuals is essential for robust analysis.

We explore the use of population-level models to integrate sparse datasets from individual patients. First, we employ nonlinear mixed-effects models to describe heterogeneous patient populations. To leverage large patient cohorts with limited information per individual, we introduce a novel inference scheme based on neural posterior approximation. Additionally, we utilize multi-state stochastic models to analyze patient trajectories, focusing on sparse longitudinal observations. Finally, we apply federated learning techniques to enhance data accessibility and integration across multiple clinical institutions.

Our application of neural posterior approximation demonstrated effective integration and analysis of large, sparse datasets, providing insights into patient heterogeneity. The multi-state stochastic models facilitated the understanding of breast cancer metastasis development and the impact on different patient subgroups. Federated learning improved data accessibility, enabling more comprehensive analysis without compromising patient privacy.