dc.contributor.author
Just, Sandra Anna
dc.contributor.author
Bröcker, Anna-Lena
dc.contributor.author
Ryazanskaya, Galina
dc.contributor.author
Nenchev, Ivan
dc.contributor.author
Schneider, Maria
dc.contributor.author
Bermpohl, Felix
dc.contributor.author
Heinz, Andreas
dc.contributor.author
Montag, Christiane
dc.date.accessioned
2024-04-26T10:22:25Z
dc.date.available
2024-04-26T10:22:25Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/43372
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-43088
dc.description.abstract
Background: Impairments in speech production are a core symptom of non-affective psychosis (NAP). While traditional clinical ratings of patients’ speech involve a subjective human factor, modern methods of natural language processing (NLP) promise an automatic and objective way of analyzing patients’ speech. This study aimed to validate NLP methods for analyzing speech production in NAP patients.
Methods: Speech samples from patients with a diagnosis of schizophrenia or schizoaffective disorder were obtained at two measurement points, 6 months apart. Out of N = 71 patients at T1, speech samples were also available for N = 54 patients at T2. Global and local models of semantic coherence as well as different word embeddings (word2vec vs. GloVe) were applied to the transcribed speech samples. They were tested and compared regarding their correlation with clinical ratings and external criteria from cross-sectional and longitudinal measurements.
Results: Results did not show differences for global vs. local coherence models and found more significant correlations between word2vec models and clinically relevant outcome variables than for GloVe models. Exploratory analysis of longitudinal data did not yield significant correlation with coherence scores.
Conclusion: These results indicate that natural language processing methods need to be critically validated in more studies and carefully selected before clinical application.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
speech analysis
en
dc.subject
automated analysis
en
dc.subject
natural language processing
en
dc.subject
artificial intelligence
en
dc.subject
schizophrenia
en
dc.subject
formal thought disorder
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Validation of natural language processing methods capturing semantic incoherence in the speech of patients with non-affective psychosis
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
1208856
dcterms.bibliographicCitation.doi
10.3389/fpsyt.2023.1208856
dcterms.bibliographicCitation.journaltitle
Frontiers in Psychiatry
dcterms.bibliographicCitation.originalpublishername
Frontiers Media SA
dcterms.bibliographicCitation.volume
14
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
37564246
dcterms.isPartOf.eissn
1664-0640