Novel pathways toward improving relapse prediction in schizophrenia [ 2018 - 2021 ]

Research Grant

[Cite as]

Brief description Relapse is a devastating problem in schizophrenia and our ability to predict when it occurs is still relatively poor. This project seeks to investigate a new method of tracking relapse by measuring speech and symptom changes across time. This novel design is supported by advanced data modelling methods to provide sensitive predictive ability. This project has the potential to significantly improve relapse prediction in schizophrenia and so support and increase beneficial outcomes for patients

Funding Amount $AUD 327,193.32

Funding Scheme Early Career Fellowships

Notes Peter Doherty Biomedical ECF

Click to explore relationships graph
Viewed: [[ro.stat.viewed]]