Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review
Abstract BackgroundThe use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated. ObjectiveThis review aims to summarize the use of NLP in mental health research, with a speci...
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JMIR Publications
2025-01-01
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| Series: | JMIR Mental Health |
| Online Access: | https://mental.jmir.org/2025/1/e67192 |
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| author | Dmitry A Scherbakov Nina C Hubig Leslie A Lenert Alexander V Alekseyenko Jihad S Obeid |
| author_facet | Dmitry A Scherbakov Nina C Hubig Leslie A Lenert Alexander V Alekseyenko Jihad S Obeid |
| author_sort | Dmitry A Scherbakov |
| collection | DOAJ |
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Abstract
BackgroundThe use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.
ObjectiveThis 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.
MethodsThe 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.
ResultsThe 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.
ConclusionsThis 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. |
| format | Article |
| id | doaj-art-8cc47062fdea4912b604287b3212aef1 |
| institution | Kabale University |
| issn | 2368-7959 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Mental Health |
| spelling | doaj-art-8cc47062fdea4912b604287b3212aef12025-01-27T04:45:17ZengJMIR PublicationsJMIR Mental Health2368-79592025-01-0112e67192e6719210.2196/67192Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping ReviewDmitry A Scherbakovhttp://orcid.org/0009-0005-7274-0934Nina C Hubighttp://orcid.org/0000-0002-8911-7832Leslie A Lenerthttp://orcid.org/0000-0002-9680-5094Alexander V Alekseyenkohttp://orcid.org/0000-0002-5748-2085Jihad S Obeidhttp://orcid.org/0000-0002-7193-7779 Abstract BackgroundThe use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated. ObjectiveThis 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. MethodsThe 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. ResultsThe 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. ConclusionsThis 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.https://mental.jmir.org/2025/1/e67192 |
| spellingShingle | Dmitry A Scherbakov Nina C Hubig Leslie A Lenert Alexander V Alekseyenko Jihad S Obeid Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review JMIR Mental Health |
| title | Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review |
| title_full | Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review |
| title_fullStr | Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review |
| title_full_unstemmed | Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review |
| title_short | Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review |
| title_sort | natural language processing and social determinants of health in mental health research ai assisted scoping review |
| url | https://mental.jmir.org/2025/1/e67192 |
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