Natural language processing for mental health interventions: a systematic review and research framework.
Journal
Translational psychiatry
ISSN: 2158-3188
Titre abrégé: Transl Psychiatry
Pays: United States
ID NLM: 101562664
Informations de publication
Date de publication:
06 10 2023
06 10 2023
Historique:
received:
07
03
2022
accepted:
04
09
2023
revised:
31
08
2023
medline:
9
10
2023
pubmed:
6
10
2023
entrez:
5
10
2023
Statut:
epublish
Résumé
Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.
Identifiants
pubmed: 37798296
doi: 10.1038/s41398-023-02592-2
pii: 10.1038/s41398-023-02592-2
pmc: PMC10556019
doi:
Types de publication
Systematic Review
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
309Subventions
Organisme : NIMH NIH HHS
ID : R44 MH124334
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH125179
Pays : United States
Organisme : Bill & Melinda Gates Foundation
ID : INV-004841
Pays : United States
Organisme : NIMH NIH HHS
ID : K23 MH134068
Pays : United States
Organisme : NCATS NIH HHS
ID : KL2 TR001446
Pays : United States
Informations de copyright
© 2023. Springer Nature Limited.
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