Linguistic markers in at-risk mental states using natural language processing : a systematic review
Zhang, Yuhan (Universitat Autònoma de Barcelona. Departament de Psicologia Clínica i de la Salut)
Carrió Asian, Alba (Universitat Autònoma de Barcelona. Departament de Psicologia Clínica i de la Salut)
Sevilla-Llewellyn-Jones, Julia (Hospital Clínico San Carlos (Madrid))
Gutiérrez, Enrique (Universidad Politécnica de Madrid. Departamento de Matemática Aplicada a las Tecnologías de la Información y las Comunicaciones)
Calvo, Ana (Universidad Complutense de Madrid. Departamento de Personalidad, Evaluación y Psicología Clínica)
Navarro, José-Blas (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de les Ciències de la Salut)
Barajas Vélez, Ana (Universitat Autònoma de Barcelona. Departament de Psicologia Clínica i de la Salut)

Data:	2026
Descripció:	20 pàg.
Resum:	Background/Objectives: In recent years, research on psychosis has increasingly focused on prevention, aiming to implement early interventions that mitigate or reduce its impact. Within this framework, the analysis of linguistic markers in individuals with at-risk mental states (ARMS) has proven valuable for identifying those at risk and predicting psychosis onset. Artificial intelligence tools, particularly natural language processing (NLP), have emerged as effective resources for detecting these language-based indicators. This study aims to synthesize the existing scientific evidence on linguistic markers analyzed through NLP techniques in individuals with ARMS. Methods: A systematic review following the PRISMA 2020 protocol was conducted. Three databases (PubMed, PsycInfo, and Scopus) were searched for published articles from their inception to October 2025. Rayyan software was used to manage references and article downloads. Out of ninety initial search results, fifteen studies involving 1313 participants from diverse groups were included in the review. Results: The findings indicated that alterations in semantic coherence, syntactic complexity, referential cohesion, and speech/content poverty differentiated ARMS individuals from healthy controls. Several of these markers, analyzed with NLP methods, predicted the onset of psychosis with accuracy levels ranging from 79% to 100%, although these findings should be interpreted with caution due to the significant methodological heterogeneity and variability in sample sizes across the included studies. Conclusions: NLP techniques offer a powerful approach for detecting language alterations that distinguish ARMS individuals and provide meaningful predictions of psychosis onset, highlighting their potential as a complement to traditional clinical assessments for early identification and prevention.
Ajuts:	Generalitat de Catalunya 2021/SGR-000534 European Commission 101126533
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	At-risk mental state ; Psychosis ; Linguistic markers ; Natural language processing ; Artificial intelligence
Publicat a:	Healthcare, Vol. 14, Num. 8 (2026) , art. 999, ISSN 2227-9032

DOI: 10.3390/healthcare14080999

20 p, 489.2 KB

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