Novel pathways toward improving relapse prediction in schizophrenia

在精神分裂症中，复发是一个毁灭性的问题，我们预测它何时发生的能力仍然相对较差。这个项目试图通过测量言语和症状的变化来研究一种追踪复发的新方法。这种新颖的设计由先进的数据建模方法支持，以提供敏感的预测能力。该项目有可能显著提高精神分裂症的复发预测，从而支持和增加患者的有益结果

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