The lecture by Markus Strauss from the CSH Vienna will take place at the Complexity Science Hub Vienna in Room 201.
If you are interested in participating, please email to email@example.com
Multimorbidity is a major health issue, especially in the elderly, and better understanding the complex higher-order interactions between diseases is a goal of this work. In this seminar talk, I present our machine learning based approach of building an interpretable predictive model to characterize the multimorbid health state of patients. The non-parametric model allows in particular the quantification of synergistic/redundant associations between disease phenotypes, or, eventually the identification of new disease phenotypes.
In this work, a disease phenotype is technically a set of diagnoses, but these phenotypes (features) gain meaning by their relation to (single) diagnoses (targets) via the coefficients of the predictive model. These model coefficients form a multiplex network where the phenotypes are the nodes and the targets make up the layers. The phenotypes within a layer form a lattice structure which allows to relate higher-order phenotypes to their lower-order phenotype neighbors. The multiplex network can be reduced to a target-target or a phenotype-phenotype network. By extracting the backbones of these networks, potentially interesting relations between diseases can be deduced. The presentation will explain the method and show some preliminary findings.