Natural Language Processing and Social Determinants of Health in Mental Health Research: An Artificial Intelligence Assisted Scoping Review (Preprint)

Abstract

BACKGROUND The usage of natural language processing (NLP) in mental health research is increasing with a wide range of applications and datasets being investigated. OBJECTIVE This review aims to summarize the usage NLP in mental health research, with a special focus on the types of text datasets and the usage of social determinants of health (SDOH) in NLP projects related to mental health. METHODS The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete. All citations were uploaded to Covidence online 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 substitute human reviewers. RESULTS The screening process, assisted by the custom LLM, led to the inclusion of 1,768 studies in the final review. The majority of the reviewed studies (n=665, 42.8%) utilized 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, 49.0%) were commonly extracted, while SDOH factors were less frequently reported, with urban/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. CONCLUSIONS 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. Discovered datasets could be used to place more emphasis on SDOH in future studies.