Natural Language Processing markers in first episode psychosis and people at clinical high-risk.

ABSTRACT: Recent work has suggested that disorganised speech might be a powerful predictor of later psychotic illness in clinical high risk subjects. To that end, several automated measures to quantify disorganisation of transcribed speech have been proposed. However, it remains unclear which measures are most strongly associated with psychosis, how different measures are related to each other and what the best strategies are to collect speech data from participants. Here, we assessed whether twelve automated Natural Language Processing markers could differentiate transcribed speech excerpts from subjects at clinical high risk for psychosis, first episode psychosis patients and healthy control subjects (total N = 54). In-line with previous work, several measures showed significant differences betwee

SUBMITTER: Morgan SE 

PROVIDER: S-EPMC8669009 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature