WP4 - Statistical Modeling of Health Data

The DMMH system collects a rich source of data and information on the overall behavior and mental state of individuals throughout the study. WP4 is dedicated to analyzing this data and extracting relevant patterns, as well as to analyzing crucial predictive features to build statistical models that can be used to guide clinical care. WP4 uses simple low level statistics, as well as modern state-of-the-art deep time series models, based on recurrent neural networks for the prediction of individual health trajectories. 

The people involved

Dr. Georgia Koppe

Head of research group Computational Psychiatry, focused on the data analytics of IMMERSE (WP4)

Prof. Daniel Dürstewitz

Head of dept. of Theoretical Neuroscience, focused on the data analytics of IMMERSE (WP4)

Prof. Peter Kuppens

Professor at the Research group of Quantitative Psychology and Individual Differences, provides guidance on developing data visualisation and analyses relevant for clinicians (WP4)

Manuel Brenner

PhD Candidate at CIMH, focused on the development of AI algorithms for WP7