Patients with first-episode psychosis (FEP) show altered speech patterns and content that could pinpoint symptom severity — and help predict future relapse, two new studies suggest.
In the first study, an algorithm was created to analyze speech patterns and semantic content to create novel "speech networks." Compared with their healthy peers, patients with FEP had smaller and more fragmented networks. At-risk individuals had fragmented values that were in between those of the FEP and healthy control groups.
"This suggests that semantic speech networks can enable deeper phenotyping of formal thought disorder and psychosis," said lead author Caroline Nettekoven, PhD, Department of Psychiatry, University of Cambridge, United Kingdom.
In the second study, Janna N. de Boer, MD, University of Groningen, the Netherlands, and colleagues examined patients with FEP who did and did not experience relapse after 24 months of follow-up.
An algorithm based on natural language processing (NLP) of speech recordings predicted the relapses with an accuracy of more than 80%.
NLP "is a powerful tool with high potential for clinical application and diagnosis and differentiation, given its ease in acquirement, low cost, and naturally low patient burden," said de Boer.
The findings for both studies were presented at the Congress of the
