Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review

• Published in: JMIR mental health Vol. 12; p. e67192
• Main Authors: Scherbakov, Dmitry A, Hubig, Nina C, Lenert, Leslie A, Alekseyenko, Alexander V, Obeid, Jihad S
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 16.01.2025
• Subjects: Artificial Intelligence; Automation; Digital Health Reviews; Grief; Humans; Information Retrieval; Large language models; Mental Health; Methods and New Tools in Mental Health Research; Natural Language Processing; Review; Social Determinants of Health; Social Media; mental health research; natural language processing; mental health; suicide; AI; datasets; LLM; large language model; artificial intelligence; automated review; NLP; automation; quantitative; depression; determinant; scoping review
• DOI: 10.2196/67192
• ISSN: 2368-7959

The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated. This review aims to summarize the use of NLP in mental health research, with a special focus on the types of text datasets and the use of social determinants of health (SDOH) in NLP projects related to mental health. The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete. All citations were uploaded to Covidence (Veritas Health Innovation) software. The screening and extraction process took place in Covidence with the help of a custom large language model (LLM) module developed by our team. This LLM module was calibrated and tuned to automate many aspects of the review process. The screening process, assisted by the custom LLM, led to the inclusion of 1768 studies in the final review. Most of the reviewed studies (n=665, 42.8%) used clinical data as their primary text dataset, followed by social media datasets (n=523, 33.7%). The United States contributed the highest number of studies (n=568, 36.6%), with depression (n=438, 28.2%) and suicide (n=240, 15.5%) being the most frequently investigated mental health issues. Traditional demographic variables, such as age (n=877, 56.5%) and gender (n=760, 49%), were commonly extracted, while SDOH factors were less frequently reported, with urban or rural status being the most used (n=19, 1.2%). Over half of the citations (n=826, 53.2%) did not provide clear information on dataset accessibility, although a sizable number of studies (n=304, 19.6%) made their datasets publicly available. This scoping review underscores the significant role of clinical notes and social media in NLP-based mental health research. Despite the clear relevance of SDOH to mental health, their underutilization presents a gap in current research. This review can be a starting point for researchers looking for an overview of mental health projects using text data. Shared datasets could be used to place more emphasis on SDOH in future studies.