Natural language processing applied to mental illness detection: a narrative review

Abstract

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people's life with impact on society's health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

Fig. 1. Overview of article selection process.

Six databases (PubMed, Scopus, Web of Science, DBLP computer science bibliography, IEEE Xplore, and ACM Digital Library) were searched. The flowchart lists reasons for excluding the study from the data extraction and quality assessment.

Fig. 2. NLP trends applied to mental illness detection research using machine learning and deep learning.

The trend of the number of articles containing machine learning-based and deep learning-based methods for detecting mental illness from 2012 to 2021.

Fig. 3. Sankey diagram of NLP methods, illness, languages and applications.

The different methods with their associated application are represented via flows. Nodes are represented as rectangles, and the height represents their value. The width of each curved line is proportional to their values.

Fig. 4. Distribution of different data sources.

The pie chart depicts the percentages of different textual data sources based on their numbers.

Fig. 5. Proportions of various types of mental illness.

The chart depicts the percentages of different mental illness types based on their numbers.