Spectral structure learning for clinical time series
The research developed StructGP, a structured Gaussian process, to model clinical time series as ordered conditional relations in a directed acyclic graph (DAG). An adapted NOTEARS algorithm was implemented to recover this underlying graph by solving continuous optimization problems. The method successfully learned dependencies between irregularly sampled patient data.