Natural Language Processing markers in first episode psychosis and people at clinical high-risk.
Vértes, Petra E
Ip, Samantha HY
De Micheli, Andrea
Spencer, Tom J
Springer Science and Business Media LLC
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Morgan, S., Diederen, K., Vértes, P. E., Ip, S. H., Wang, B., Thompson, B., Demjaha, A., et al. (2021). Natural Language Processing markers in first episode psychosis and people at clinical high-risk.. Transl Psychiatry https://doi.org/10.1038/s41398-021-01722-y
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.
SEM was supported by the Accelerate Programme for Scientific Discovery, funded by Schmidt Futures, a Fellowship from The Alan Turing Institute, London, and a Henslow Fellowship at Lucy Cavendish College, University of Cambridge, funded by the Cambridge Philosophical Society. PEV is supported by a fellowship from MQ: Transforming Mental Health (MQF17_24). This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), the UK Medical Research Council (MRC) and the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.
Alan Turing Institute (R-CAM-006)
MQ: Transforming Mental Health (MQ17-24 Vertes)
External DOI: https://doi.org/10.1038/s41398-021-01722-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329586
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