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Natural Language Processing markers in First Episode Psychosis and People at Clinical High-risk

Paper Details

Published: 2021/12/13

Journal: Translational Psychiatry

Volume: 11

Number: 630

DOI: 10.1038/s41398-021-01722-y

Description:

Disorganised speech can help predict later psychotic illness. This paper assesses the performance of twelve automated Natural Language Processing markers in differentiating transcribed speech excerpts from subjects at clinical high risk for psychosis, first episode psychosis patients and healthy control subjects.

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 between groups, including semantic coherence, speech graph connectivity and a measure of whether speech was on-topic, the latter of which outperformed the related measure of tangentiality. Most NLP measures examined were only weakly related to each other, suggesting they provide complementary information. Finally, we compared the ability of transcribed speech generated using different tasks to differentiate the groups. Speech generated from picture descriptions of the Thematic Apperception Test and a story re-telling task outperformed free speech, suggesting that choice of speech generation method may be an important consideration. Overall, quantitative speech markers represent a promising direction for future clinical applications.

Authors

Sarah E. Morgan

Kelly Diederen

Petra E. Vértes

Samantha H. Y. Ip

Bo Wang

Bethany Thompson

Arsime Demjaha

Andrea Micheli

Dominic Oliver

Maria Liakata

Paolo Fusar-Poli

Tom J. Spencer

Philip McGuire