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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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On the Effectiveness of Radio Environmental Map Prediction Through Image-Based Deep Learning
Hosseinzadeh, M., Barbiroli, M., Vitucci, E. M., Degli‐Esposti, V., & Fuschini, F. (2026) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2026.3676109 · Cited by 3
“In recent years, ML has demonstrated remarkable success in fields such as computer vision (object detection or recognition), natural language processing, and biomedical analysis [32], [33], [34].”
Saarikivi, J., Hjellvik, V., Tiihonen, J., Lähteenvuo, M., Taipale, H. T., Odsbu, I., & Lieslehto, J. (2026) · Schizophrenia Research · doi:10.1016/j.schres.2026.03.013 · Cited by 1
Transforming depression research: The role of artificial intelligence and computational modelling
Somkuwar, S. S., Hussain, U., Sahu, D. C., Sheikh, D. M. T., Sheikh, S. M., Gautam, M. M., Meshram, S. S., & Belgamwar, V. S. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100492 · Cited by 1
Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review
Rao, S., Chen, X. R., Deng, G., Xie, J., Jiang, T., Li, T., Zhang, Y., & Jiang, H. (2026) · JMIR Medical Informatics · doi:10.2196/91249 · Cited by 2
“…reviews do not incorporate the recent breakthroughs, such as the development and application of attention mechanisms, transformers, and pretrained LLMs. Reviews such as Le Glaz et al [19] primarily focus on traditional NLP and machine learning (ML) techniques applied across mental health domains.”
Artificial intelligence (AI) literature in patents: a global landscape
Priyanka, P., Azad, S., & Chakravarty, R. (2021) · Library Hi Tech News · doi:10.1108/lhtn-09-2021-0062 · Cited by 1
Classification of Construction Accident Court Cases Via Natural Language Processing in Hong Kong
Li, R. Y. M., Li, H. C. Y., Tang, B., & Au, W. C. (2022) · Springer eBooks · doi:10.1007/978-981-19-0737-1_5 · Cited by 1
Understanding the Patients’ Adoption and Usage of AI Solution in Mental Health: A Scoping Review
Nguyen, N., Labonté-LeMoyne, É., Grégoire, Y., Radanielina-Hita, M. L., & Sénécal, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-19682-9_85 · Cited by 1
An effective hybrid automated Chinese scoring system for medical education
Sun, R., Li, X., Shen, J., & Jin, W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121114 · Cited by 1
Campos, J. R., Machado, R., & Vieira, M. P. A. (2023) · Latin-American Symposium on Dependable Computing · doi:10.1145/3615366.3615423 · Cited by 1
Exploring Emotion and Sentiment Landscape of Depression: A Multimodal Analysis Approach
Anekar, D., Deshpande, Y. D., Suryawanshi, R. S., Waman, R., Divekar, V., & Salunke, R. (2023) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat59970.2023.10353344 · Cited by 1
“Aziliz Le Glaz et al., [7] have proposed a systematic review's major objective was to express, in both procedural and practical terms, the research that employed ML and NLP approaches for psychological health.”
BackgroundAnekar, Y. D. D. (2024) · Journal of Electrical Systems · doi:10.52783/jes.781 · Cited by 1
“Taylor C Ryan et al., (2021)[25] have proposed a systematic review with the primary objective of elucidating and defining, both operationally and practically, research utilizing Machine Learning (ML) and Natural Language Processing (NLP) approaches for psychological health.”
BackgroundSchneider, P., Mikołajewski, D., Bryniarska, A., Igraś-Cybulska, M., Cybulski, A., Marcinowicz, W., Janiszewski, M., & Kawala‐Sterniuk, A. (2024) · Progress in Applied Electrical Engineering (PAEE) · doi:10.1109/paee63906.2024.10701433 · Cited by 2
Artificial intelligence: Blessing or curse?
Briganti, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23655-6.00005-8 · Cited by 1
AI and Its Importance in the Development of the Luxury Market
Ellahi, A. (2025) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-8542-5.ch016 · Cited by 1
Considerations and Challenges When Using Clinical and Vital Record Review for Suicide Research
Arias, S. A., Gaudiano, B. A., Epstein-Lubow, G., Zylberfuden, S., & Weinstock, L. (2025) · Journal of patient safety · doi:10.1097/pts.0000000000001325 · Cited by 1
Rapisarda, F., Lanovaz, M. J., Guay, S., & Geoffrion, S. (2025) · International Journal of Mental Health · doi:10.1080/00207411.2025.2486084 · Cited by 1
Ghiloufi, H., Merveille, N., & Mellouli, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-82896-6_7 · Cited by 1
Merayo, N., Lanchares, A. A., & González‐Sanguino, C. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3568074 · Cited by 1
“On the one hand, the application of these techniques in these contexts has mainly focused on the detection of mental disorders [17], [18], rather than assessing its impact on society, using clinical samples of diagnosed individuals or social network users [19].”
Singh, N. T., Aryan, A., Dhiman, R., & Rana, M. (2025) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv64824.2025.11085533 · Cited by 2
Text Mining and Natural Language Processing for Health Informatics
Anoop, V. S. (2021) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-7188-0.ch008 · Cited by 1
Tri-model ensemble with Grid Search optimization for Bipolar Disorder Diagnosis
Zain, S. M., & Mumtaz, W. (2022) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit57066.2022.00015 · Cited by 1
Magoč, T., Everson, R., & Harle, C. A. (2023) · Journal of Clinical and Translational Science · doi:10.1017/cts.2023.575 · Cited by 1
Digital Interventions for Mental Health Problems
Zhang, H., & Fu, G. (2023) · International Journal of Managerial Studies and Research · doi:10.20431/2349-0349.1108007 · Cited by 1
“…shown that when people think they are talking to a computer rather than a real clinician, they have less fear of self-disclosure, less impression management behavior, are more likely to express the severity of their emotions, and are more willing to self-disclosure (Le Glaz Aziliz et al., 2021).”
Health Data Science and Semantic Technologies
Ayatollahi, H. (2022) · doi:10.1002/9781119865339.ch12 · Cited by 1
Liu, Y. (2023) · Research Square · doi:10.21203/rs.3.rs-2910523/v1 · Cited by 1
Quillivic, R., Gayraud, F., Auxéméry, Y., Vanni, L., Peschanski, D., Eustache, F., Dayan, J., & Mesmoudi, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3747882/v1 · Cited by 1
Stammers, M., Ramgopal, B., Obeng, A., Vyas, A., Nouraei, S. A. R., Metcalf, C., Batchelor, J., Shepherd, J., & Gwiggner, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4249448/v1 · Cited by 1
Novoa-Laurentiev, J., Curtin-Bowen, M., Pullman, A., Song, W., Syrowatka, A., Chen, J., Sainlaire, M., CHANG, F. Y., Gray, K., Panta, P., et al. (2024) · doi:10.2196/preprints.63720 · Cited by 1
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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