Natural language processing for mental health interventions: a systematic review and research framework

Area of Science:

• Computational linguistics and digital mental health.
• Artificial intelligence applications in healthcare.

Background:

• Neuropsychiatric disorders incur significant societal costs, with treatment efficacy often limited by the absence of objective outcome and fidelity measures.
• Natural Language Processing (NLP) offers a novel approach to analyze mental health interventions (MHI) by processing conversational data, yet its full clinical and research potential remains underexplored.

Approach:

• A systematic review adhering to PRISMA guidelines was conducted, analyzing 102 articles on NLP-MHI studies published up to January 2023.
• Studies were evaluated based on computational characteristics (NLP algorithms, features, pipelines, metrics) and clinical aspects (ground truths, samples, focus, limitations).
• Data sources included PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv, encompassing peer-reviewed AI conference manuscripts.

Key Points:

• NLP-MHI research has rapidly expanded since 2019, with increasing sample sizes and the adoption of large language models, primarily utilizing data from digital health platforms.
• Text-based features demonstrated higher accuracy than audio markers in NLP models.
• Common clinical focuses included patient presentation, intervention response, monitoring, provider characteristics, and relational dynamics, with clinician ratings, patient self-reports, and rater annotations serving as primary ground truths.

Conclusions:

• Despite advancements, limitations such as lack of linguistic diversity, restricted reproducibility, and population bias persist in NLP-MHI research.
• A validated research framework (NLPxMHI) is proposed to guide computational and clinical researchers in addressing these gaps.
• The framework aims to enhance the clinical utility, data accessibility, and fairness of NLP applications in mental health interventions.