Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia.
artificial intelligence
machine learning
natural language processing
phenomenology
psychosis
self-disturbance
Journal
Schizophrenia bulletin
ISSN: 1745-1701
Titre abrégé: Schizophr Bull
Pays: United States
ID NLM: 0236760
Informations de publication
Date de publication:
15 03 2023
15 03 2023
Historique:
pubmed:
3
10
2022
medline:
21
3
2023
entrez:
2
10
2022
Statut:
ppublish
Résumé
Disturbances in self-experience are a central feature of schizophrenia and its study can enhance phenomenological understanding and inform mechanisms underlying clinical symptoms. Self-experience involves the sense of self-presence, of being the subject of one's own experiences and agent of one's own actions, and of being distinct from others. Self-experience is traditionally assessed by manual rating of interviews; however, natural language processing (NLP) offers automated approach that can augment manual ratings by rapid and reliable analysis of text. We elicited autobiographical narratives from 167 patients with schizophrenia or schizoaffective disorder (SZ) and 90 healthy controls (HC), amounting to 490 000 words and 26 000 sentences. We used NLP techniques to examine transcripts for language related to self-experience, machine learning to validate group differences in language, and canonical correlation analysis to examine the relationship between language and symptoms. Topics related to self-experience and agency emerged as significantly more expressed in SZ than HC (P < 10-13) and were decoupled from similarly emerging features such as emotional tone, semantic coherence, and concepts related to burden. Further validation on hold-out data showed that a classifier trained on these features achieved patient-control discrimination with AUC = 0.80 (P < 10-5). Canonical correlation analysis revealed significant relationships between self-experience and agency language features and clinical symptoms. Notably, the self-experience and agency topics emerged without any explicit probing by the interviewer and can be algorithmically detected even though they involve higher-order metacognitive processes. These findings illustrate the utility of NLP methods to examine phenomenological aspects of schizophrenia.
Sections du résumé
BACKGROUND AND HYPOTHESIS
Disturbances in self-experience are a central feature of schizophrenia and its study can enhance phenomenological understanding and inform mechanisms underlying clinical symptoms. Self-experience involves the sense of self-presence, of being the subject of one's own experiences and agent of one's own actions, and of being distinct from others. Self-experience is traditionally assessed by manual rating of interviews; however, natural language processing (NLP) offers automated approach that can augment manual ratings by rapid and reliable analysis of text.
STUDY DESIGN
We elicited autobiographical narratives from 167 patients with schizophrenia or schizoaffective disorder (SZ) and 90 healthy controls (HC), amounting to 490 000 words and 26 000 sentences. We used NLP techniques to examine transcripts for language related to self-experience, machine learning to validate group differences in language, and canonical correlation analysis to examine the relationship between language and symptoms.
STUDY RESULTS
Topics related to self-experience and agency emerged as significantly more expressed in SZ than HC (P < 10-13) and were decoupled from similarly emerging features such as emotional tone, semantic coherence, and concepts related to burden. Further validation on hold-out data showed that a classifier trained on these features achieved patient-control discrimination with AUC = 0.80 (P < 10-5). Canonical correlation analysis revealed significant relationships between self-experience and agency language features and clinical symptoms.
CONCLUSIONS
Notably, the self-experience and agency topics emerged without any explicit probing by the interviewer and can be algorithmically detected even though they involve higher-order metacognitive processes. These findings illustrate the utility of NLP methods to examine phenomenological aspects of schizophrenia.
Identifiants
pubmed: 36184074
pii: 6731748
doi: 10.1093/schbul/sbac126
pmc: PMC10016400
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
444-453Subventions
Organisme : NIMH NIH HHS
ID : R01 MH107558
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH115332
Pays : United States
Organisme : NCATS NIH HHS
ID : KL2 TR001106
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001108
Pays : United States
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.
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