Algorithm Predicts Psychosis Risk for At-Risk Kids - Technology Networks
Teams from the UNIGE and EPFL have used for the first time the method of longitudinal network analysis applied to children, in order to detect the symptoms that herald the development of psychotic illness in the future. One third of children with a microdeletion of chromosome 22 will later develop a psychotic illness such as schizophrenia. But how do we know which of these children might be affected? Today, various studies have contributed to the understanding of the neurobiological mechanisms that are associated with the development of psychotic illnesses. The problem is that the ability to identify those at risk and adapt their treatment accordingly remains limited. Indeed, many variables - other than neurobiological - contribute to their development. This is why a team from the University of Geneva has joined forces with a team from the EPFL to use in a longitudinal manner an artificial intelligence tool: the network analysis method. This algorithm correlates many variable...