In diesem Wintersemester 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 17:30 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.

The primary aim of this talk to develop and apply biostatistical methods to improve predictive modeling of data for precision medicine. In order to achieve such a solid framework for different type of datasets the selection of methods are important. However, currently their is no universal method which can be applied for data gathered from humans. To understand the given data at hand, first statistical tools which considers the distribution of the data namely Bayesian posterior distribution will be discussed. Second, to counter one of the most important problems in most of the clinical datasets is the demographics namely matching cohorts for sex would be considered with propensity score matching analyses. In order to model complex datasets, with several input and output variables the structural equation modelling will be delibrated. After understanding and modelling the datasets the prediction will be covered with some machine and deep learning approaches and finally some applications to datasets from multimodal, longitudinal and signal based analyses will be explored. For each methodological aspect an example will be provided with obtained results.  The applications will be highlighted with examples and corresponding results. Taken together, the integration of methods leading to the individualized prediction of each subject will be demonstrated.