TY  - JOUR
AU  - Teferra, Bazen Gashaw
AU  - Rueda, Alice
AU  - Pang, Hilary
AU  - Valenzano, Richard
AU  - Samavi, Reza
AU  - Krishnan, Sridhar
AU  - Bhat, Venkat
PY  - 2024
DA  - 2024/11/4
TI  - Screening for Depression Using Natural Language Processing: Literature Review
JO  - Interact J Med Res
SP  - e55067
VL  - 13
KW  - depression
KW  - natural language processing
KW  - NLP
KW  - sentiment analysis
KW  - machine learning
KW  - deep learning
KW  - transformer-based models
KW  - large language models
KW  - cross-cultural
KW  - research domain criteria
KW  - RDoC
AB  - Background: Depression is a prevalent global mental health disorder with substantial individual and societal impact. Natural language processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but there are challenges and ethical considerations. Objective: This literature review aims to explore existing NLP methods for detecting depression, discuss successes and limitations, address ethical concerns, and highlight potential biases. Methods: A literature search was conducted using Semantic Scholar, PubMed, and Google Scholar to identify studies on depression screening using NLP. Keywords included “depression screening,” “depression detection,” and “natural language processing.” Studies were included if they discussed the application of NLP techniques for depression screening or detection. Studies were screened and selected for relevance, with data extracted and synthesized to identify common themes and gaps in the literature. Results: NLP techniques, including sentiment analysis, linguistic markers, and deep learning models, offer practical tools for depression screening. Supervised and unsupervised machine learning models and large language models like transformers have demonstrated high accuracy in a variety of application domains. However, ethical concerns related to privacy, bias, interpretability, and lack of regulations to protect individuals arise. Furthermore, cultural and multilingual perspectives highlight the need for culturally sensitive models. Conclusions: NLP presents opportunities to enhance depression detection, but considerable challenges persist. Ethical concerns must be addressed, governance guidance is needed to mitigate risks, and cross-cultural perspectives must be integrated. Future directions include improving interpretability, personalization, and increased collaboration with domain experts, such as data scientists and machine learning engineers. NLP’s potential to enhance mental health care remains promising, depending on overcoming obstacles and continuing innovation. 
SN  - 1929-073X
UR  - https://www.i-jmr.org/2024/1/e55067
UR  - https://doi.org/10.2196/55067
DO  - 10.2196/55067
ID  - info:doi/10.2196/55067
ER  -