JMIR Publications

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.