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Machine Learning and Natural Language Processing in Mental Health: Systematic Review
Open accessPublished version of a preprint
Abstract
BACKGROUND: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. OBJECTIVE: The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice. METHODS: This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. RESULTS: A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. CONCLUSIONS: Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.
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“Such NLP tools and methods can support mental health research and care delivery in clinical practice [24].”
MethodApplication of Machine Learning in FPGA EDA Tool Development
Goswami, P., & Bhatia, D. K. (2023) · IEEE Access · doi:10.1109/access.2023.3322358 · Cited by 30
“ML has seen its applications in various domains like image recognition [7], [8], natural language processing [9], [10], [11], audio and signal processing [12], [13], [14], etc.”
BackgroundRevolutionizing Healthcare Platforms: The Impact of AI on Patient Engagement and Treatment Efficacy
Manishkumar, P. B. D. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24201070211 · Cited by 29
War, emotions, mental health, and artificial intelligence
Ćosić, K., Kopilaš, V., & Jovanović, T. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1394045 · Cited by 58
“The predictive AI-based methods which can recognize potential chronic psychopathology early enough compared to traditional reactive psychiatry is example of more proactive and preventive type of medicine (Ćosić et al., 2020a,b, 2021; Bertl et al., 2022).”
MethodScherbakov, D., Hubig, N., Lenert, L. A., Alekseyenko, A. V., & Obeid, J. S. (2024) · JMIR Mental Health · doi:10.2196/67192 · Cited by 34
Accuracy of Large Language Models for Literature Screening in Thoracic Surgery: Diagnostic Study
Dai, Z.-Y., Wang, F., Shen, C., Ji, Y.-L., Li, Z.-Y., Wang, Y., & Pu, Q. (2025) · Journal of Medical Internet Research · doi:10.2196/67488 · Cited by 39
“While machine learning has shown promise in streamlining literature retrieval, its accuracy often falls short of desired standards [7-9].”
Keikha, A. (2022) · Soft Computing · doi:10.1007/s00500-022-06995-z · Cited by 29
“GHFNs can be used in various fields, such as mechanism design, performance evaluation of employees/organizations/companies, future studies, stock markets, future markets, supply chain, society networks, cosmology, medical diagnostic and telemedicine (Glaz et al. 2021), decision making, and planning.”
BackgroundMental Health Analysis of Employees using Machine Learning Techniques
Sujal, B. H., Neelima, K., Chandrasekaran, D., Bhuvanashree, P., Duraipandian, K., Rajan, S., & Sathiyanarayanan, M. (2022) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets53615.2022.9668526 · Cited by 31
Weisenburger, R. L., Mullarkey, M. C., Labrada, J., Labrousse, D., Yang, M. Y., Huff, A. J., Hsu, K. J., Ugail, H., Shumake, J., & Beevers, C. G. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.212 · Cited by 31
Chadaga, K., Prabhu, S., Sampathila, N., Chadaga, R., Bhat, D., Sharma, A., & Swathi, K. S. (2024) · SLAS TECHNOLOGY · doi:10.1016/j.slast.2024.100129 · Cited by 28
Dhanda, S. S., Panwar, D., Lin, C., Sharma, T. K., Rastogi, D., Bindewari, S., Singh, A. M., Li, Y., Agarwal, N. K., & Agarwal, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01201-x · Cited by 35
Applications of Speech Analysis in Psychiatry
Dikaios, K., Rempel, S., Dumpala, S. H., Oore, S., Kiefte, M., & Uher, R. (2023) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000356 · Cited by 31
“U zation is no longer often used, word frequencies can contribute to classifying disorders through newer methods such as natural language processing.(81) Multifaceted semantic features have been shown to differentiate among psychotic illnesses.”
BackgroundArtificial Intelligence for the development of qualitative studies
Gamboa, A. J. P., & Díaz‐Guerra, D. D. (2023) · LatIA · doi:10.62486/latia20234 · Cited by 31
Yang, L., Wang, T., Zhang, J., Kang, S., Xu, S., & Wang, K. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01218-3 · Cited by 29
Ibrahim, S. T., Li, M., Patel, J., & Katapally, T. R. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109859 · Cited by 34
LSTM-assisted optical fiber interferometric sensing: breaking the limitation of free spectral range
Hu, J., Zhang, S., Cai, M., Ma, M., Li, S., Chen, H., & Yang, S. (2025) · Light Science & Applications · doi:10.1038/s41377-025-02008-4 · Cited by 26
Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis
Zhu, J., Li, Z., Zhang, X., Zhang, Z., & Hu, B. (2023) · Journal of Medical Internet Research · doi:10.2196/45777 · Cited by 29
“For more research on the application of text analysis and natural language processing methods in health care, see [53,54].”
Innovative deep learning techniques for monitoring aggressive behavior in social media posts
Han, H., Asif, M., Awwad, E. M., Sarhan, N. M., Ghadi, Y. Y., & Xu, B. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-023-00577-6 · Cited by 36
Romagnoli, A., Ferrara, F., Langella, R., & Zovi, A. (2024) · Pharmaceutical Research · doi:10.1007/s11095-024-03685-3 · Cited by 28
AI Quality Standards in Health Care: Rapid Umbrella Review
Kuziemsky, C. E., Chrimes, D., Minshall, S. R., Mannerow, M., & Lau, F. (2024) · Journal of Medical Internet Research · doi:10.2196/54705 · Cited by 30
Large Language Models in Psychiatry: Current Applications, Limitations, and Future Scope
Liu, Z., Bao, Y., Zeng, S., Qian, R., Deng, M., Gu, A., Li, J., Wang, W., Cai, W., Li, W., et al. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020046 · Cited by 25
Allen, K. S., Hood, D. R., Cummins, J., Kasturi, S., Mendonca, E. A., & Vest, J. R. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad024 · Cited by 25
“NLP has been successfully applied to clinical conditions including cancer,(13) cardiovascular issues,(14) and mental health.(15) Nevertheless, the use of NLP to identify social factors may pose greater challenges.”
MethodLanguage as a Window Into the Altered State of Consciousness Elicited by Psychedelic Drugs
Tagliazucchi, E. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.812227 · Cited by 25
Large Language Models for Mental Health Applications: Systematic Review (Preprint)Preprint
Guo, Z., Lai, A. G., Thygesen, J. H., Farrington, J., Keen, T., & Li, K. (2024) · doi:10.2196/preprints.57400 · Cited by 30
“The existing literature includes a review of the application of ML and NLP in mental health [47], analyses of LLMs in medicine[32], and a scoping review of LLMs in mental health.”
BackgroundMethodQuillivic, R., Gayraud, F., Auxéméry, Y., Vanni, L., Peschanski, D., Eustache, F., Dayan, J., & Mesmoudi, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-61557-7 · Cited by 24
Incorporation of artificial intelligence into nursing research: A scoping review
Yasin, Y. M., Al‐Hamad, A., Metersky, K., & Kehyayan, V. (2025) · International Nursing Review · doi:10.1111/inr.13013 · Cited by 21
“These capabilities are part of a growing trend where natural language processing and machine learning are leveraged to analyze large and complex data sets, offering innovative approaches for deriving valuable insights (Le Glaz et al., 2021).”
Gulf Countries’ Citizens’ Acceptance of COVID-19 Vaccines—A Machine Learning Approach
Alabrah, A. A., Alawadh, H. M., Okon, O. D., Meraj, T., & Rauf, H. T. (2022) · Mathematics · doi:10.3390/math10030467 · Cited by 23
Liu, Z., Peach, R. L., Lawrance, E., Noble, A., Ungless, M. A., & Barahona, M. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.779091 · Cited by 24
Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire
Skaik, R., & Inkpen, D. Z. (2022) · IEEE Access · doi:10.1109/access.2022.3208470 · Cited by 23
Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis
Zhang, Z., Zhu, J., Guo, Z., Zhang, Y., Li, Z., & Hu, B. (2024) · JMIR Mental Health · doi:10.2196/58259 · Cited by 26
“With the rapid development of natural language processing (NLP) and deep learning, many scholars have explored applying these technologies to depression detection tasks on social media [20].”
BackgroundTaiwo, O., & Al‐Bander, B. (2025) · Cognitive Computation and Systems · doi:10.1049/ccs2.12116 · Cited by 27
Wright-Berryman, J. L., Cohen, J., Haq, A., Black, D. P., & Pease, J. L. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1143175 · Cited by 22
“Natural language processing (NLP) and machine learning (ML) have expanded how mental health conditions can be identified (5, 6).”
BackgroundDepression clinical detection model based on social media: a federated deep learning approach
Liu, Y. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05754-7 · Cited by 24
“Not surprisingly, the common problem is that researchers use datasets that are small or too specific [7].”
Zheng, C., Tao, Y., Zhang, J., Xun, L., Li, T., & Yan, Q. Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127700 · Cited by 25
Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study
Bouktif, S., Khanday, A. M. U. D., & Ouni, A. (2025) · Journal of Medical Internet Research · doi:10.2196/65434 · Cited by 21
Metadata
- Authors
- Le Glaz, A., Haralambous, Y., Kim-Dufor, D.-H., Lenca, P., Billot, R., Ryan, T. C., Marsh, J., DeVylder, J., Walter, M., Berrouiguet, S., & Lemey, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Centre Hospitalier Régional Universitaire de Brest (FR)
- Johns Hopkins University (US)
- Fordham University (US)
- Université de Bretagne Occidentale (FR)
- Corresponding author
- Lemey, Christophe
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2021-05-04
- Volume
- 23
- Issue
- 5
- Pages
- e15708
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8132982
- OpenAlex
- W3089862415
- Cited by
- 576
- References
- 109
- Citations by year
- 2026: 102 · 2025: 155 · 2024: 144 · 2023: 105 · 2022: 57 · 2021: 13
- FWCI
- 7.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Oskar-Bordeaux (Universite de Bordeaux) (submittedVersion, cc-by)
- Funders
- Agence Nationale de la Recherche (ANR-18-RHUS-0014, ANR18)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- machine learning, natural language processing, mental health, mental illness, symptom extraction, treatment effectiveness, social media, electronic health records, clinical practice, artificial intelligence in psychiatry, language-specific features, nosography, clinical decision support
- MeSH terms
- Machine Learning, Data Management, Artificial Intelligence, Humans, Mental Health, Natural Language Processing
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