BACKGROUND
Depression is a prevalent global mental health disorder with substantial individual and societal impacts. Natural Language Processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but challenges and ethical considerations exist.
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
The review synthesizes research on NLP techniques, historical NLP development, depression detection methods, classification models, datasets, and ethical considerations. Cross-cultural and multilingual perspectives are discussed, along with the integration of depression screening in the RDoC framework. The review also examines validation and evaluation metrics, ethical challenges, and future directions.
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 revolutionize depression detection, but significant 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, ML engineers, etc. NLP's potential to enhance mental health care remains promising, depending on overcoming obstacles and continuing innovation.
