Machine learning and natural language processing in mental health: systematic review

Le Glaz, Aziliz; Haralambous, Yannis; Kim-Dufor, Deok-Hee; Lenca, Philippe; Billot, Romain; Ryan, Taylor C; Marsh, Jonathan; DeVylder, Jordan; Walter, Michel; Berrouiguet, Sofian; Lemey, Christophe

doi:10.2196/15708

Cited by 577Mental Health via WritingArtificial Intelligence in HealthcareSentiment Analysis and Opinion Mining

Machine learning and natural language processing in mental health: systematic review

Open access

Le Glaz, Aziliz, Haralambous, Yannis, Kim-Dufor, Deok-Hee, Lenca, Philippe, Billot, Romain, Ryan, Taylor C, Marsh, Jonathan, DeVylder, Jordan +3 more·2021·Journal of Medical Internet Research

DOI 10.2196/15708PMC PMC8132982

Published version of a preprint

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.2196/15708

Cites

109 of 109 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation
Shamseer, L., Moher, D., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & the PRISMA-P Group (2015) · BMJ · doi:10.1136/bmj.g7647 · Cited by 13,120 · see citing works
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816 · see citing works
Research domain criteria (RDoC): toward a new classification framework for research on mental disorders
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067 · cited 2× · see citing works
“The former includes data on genetics and neuroscience in its classification of mental health disorders, whereas the latter is solely based on clinical data [107].”
An introduction to latent semantic analysis
Landauer, T. K., Foltz, P. W., & Laham, D. (1998) · Discourse Processes · doi:10.1080/01638539809545028 · Cited by 4,880 · cited 1× · see citing works
“…new matrix represent aggregates of words and aggregates of documents where each dimension is not interpretable per se, but when words or documents are represented as vectors in this new latent system of coordinates, the scalar product of vectors can be used as a semantic relatedness measure [34].”
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · cited 1× · see citing works
“Indeed, ML is widely used in critical disease models in cardiology, neurology, and diabetes research [6] to automatically identify heart disease risk factors [7], to classify primary progressive aphasia subtypes [8], and for the characterization and diagnosis of cognitive impairments [9], diabetes,…”
Distributional Structure
Harris, Z. S. (1954) · <i>WORD</i> · doi:10.1080/00437956.1954.11659520 · Cited by 4,807 · see citing works
Motivational Interviewing: Helping People Change
Tober, G. (2013) · Alcohol and Alcoholism · doi:10.1093/alcalc/agt010 · Cited by 4,267 · see citing works
Machine Learning in Medicine
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · cited 1× · see citing works
“At the same time, even though thousands of papers have been published using medical data, very few have made meaningful contributions to clinical practices [111].”
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338 · see citing works
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · cited 1× · see citing works
“For example, they can help doctors improve their diagnoses, forecast disease outbreaks, and customize treatments [19,20], provide better patient care [21], and predict the splicing activity of individual exons and chromatin marks from DNA sequences [22].”
Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284 · see citing works
Applications of Machine Learning in Cancer Prediction and Prognosis
Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,113 · see citing works
Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047 · cited 1× · see citing works
“Indeed, mental health care is also benefiting from the advancements in ML [27-29].”
It's only a computer: virtual humans increase willingness to disclose
Lucas, G. M., Gratch, J., King, A., & Morency, L.-P. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.04.043 · Cited by 977 · cited 2× · see citing works
“In addition, Lucas et al [51] demonstrated through a clinical trial that when people believed they were interacting with a computer rather than an actual clinician, they reported less fear of self-disclosure, reported reduced impression management behaviors, experienced more ease in expressing the…”
Applications of machine learning in cancer prediction and prognosis.
Cruz, J. A., & Wishart, D. S. (2007) · PubMed Central · Cited by 879 · see citing works
Automated analysis of free speech predicts psychosis onset in high-risk youths
Bedi, G., Carrillo, F., Cecchi, G. A., Slezak, D. F., Sigman, M., Mota, N. B., Ribeiro, S., Javitt, D. C., Copelli, M., & Corcoran, C. M. (2015) · npj Schizophrenia · doi:10.1038/npjschz.2015.30 · Cited by 673 · cited 4× · see citing works
“Each community has a central node: Perlis et al [55] are cited in 7 other papers, Jackson et al [73] are cited in 4 other papers, Carrell et al [74] and Afshar et al [75] are cited in 2 other papers, and Bedi et al [39] are cited in 2 other papers.”
Influential
Twitter as a Tool for Health Research: A Systematic Review
Sinnenberg, L. E., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L., & Merchant, R. M. (2016) · American Journal of Public Health · doi:10.2105/ajph.2016.303512 · Cited by 666 · cited 1× · see citing works
“For instance, Sinnenberg et al [36] published a systematic review about Twitter as a tool for health research that included 137 different studies and analyzed over 5 billion tweets using ML; Castro et al [37] have processed 4.2 million EHRs spanning a period of over 20 years.”
Influential
Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation
Belsher, B. E., Smolenski, D. J., Pruitt, L. D., Bush, N. E., Beech, E. H., Workman, D. E., Morgan, R. L., Evatt, D. P., Tucker, J., & Skopp, N. A. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0174 · Cited by 540 · see citing works
Natural language processing of social media as screening for suicide risk
Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018) · Biomedical Informatics Insights · doi:10.1177/1178222618792860 · Cited by 506 · cited 4× · see citing works
“An exception to this is the group of OurDataHelps.org users [77] who volunteered to participate in scientific research and filled out a questionnaire to provide information about themselves.”
Influential
Machine learning for email spam filtering: review, approaches and open research problems
Dada, E., Bassi, J. S., Chiroma, H., Abdulhamid, S., Adetunmbi, A., & Ajibuwa, O. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e01802 · Cited by 490 · see citing works
Machine Learning and the Profession of Medicine
Darcy, A. M., Louie, A. K., & Roberts, L. W. (2016) · JAMA · doi:10.1001/jama.2015.18421 · Cited by 436 · cited 2× · see citing works
“…NLP can help identify people at clinical risk for depression or suicidal ideation, who most likely do not have access to mental health providers and/or a primary care doctor [61]; however, this reduces confidentiality protection and can lead to increased vulnerability in certain populations [21].”
Prediction of psychosis across protocols and risk cohorts using automated language analysis
Corcoran, C. M., Carrillo, F., Slezak, D. F., Bedi, G., Klim, C. C., Javitt, D. C., Bearden, C. E., & Cecchi, G. (2018) · World Psychiatry · doi:10.1002/wps.20491 · Cited by 381 · cited 1× · see citing works
“…(PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85]; eating disorders (2 cases) [89,96]; and bipolar disorder (2 cases) [37,102].”
A fully automated conversational agent for promoting mental well-being: a pilot RCT using mixed methods
Ly, K. H., Ly, A.-M., & Andersson, G. (2017) · Internet Interventions · doi:10.1016/j.invent.2017.10.002 · Cited by 368 · see citing works
Researching Mental Health Disorders in the Era of Social Media: Systematic Review
Wongkoblap, A., Vadillo, M., & Curcin, V. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7215 · Cited by 261 · cited 1× · see citing works
“Although ML and NLP provide new tools and strategies for psychiatric research and practice [52], it should be kept in mind that their use frequently raises ethical and legal concerns over consent to personal data use and data anonymization.”
Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms
Dhahri, H., Maghayreh, E. A., Mahmood, A., Elkilani, W. S., & Faisal, M. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/4253641 · Cited by 240 · cited 1× · see citing works
“…disease models in cardiology, neurology, and diabetes research [6] to automatically identify heart disease risk factors [7], to classify primary progressive aphasia subtypes [8], and for the characterization and diagnosis of cognitive impairments [9], diabetes, and cardiovascular disorders [10-17].”
Automated classification of primary progressive aphasia subtypes from narrative speech transcripts
Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014) · Cortex · doi:10.1016/j.cortex.2012.12.006 · Cited by 229 · cited 1× · see citing works
“…disease models in cardiology, neurology, and diabetes research [6] to automatically identify heart disease risk factors [7], to classify primary progressive aphasia subtypes [8], and for the characterization and diagnosis of cognitive impairments [9], diabetes, and cardiovascular disorders [10-17].”
A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care
Alanazi, H. O., Abdullah, A. H., & Qureshi, K. N. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0715-6 · Cited by 219 · cited 1× · see citing works
“Using ML in general and NLP methods in particular, one can create semiautomated systems (operating under human supervision) aiming to improve the specificity of diagnosis, knowledge of psychophysiology, speed of diagnosis, and more accurate estimations of disease severity [48].”
Diabetes Prediction Using Different Machine Learning Approaches
Sonar, P., & Jayamalini, K. (2019) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819841 · Cited by 206 · see citing works
Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model
Perlis, R. H., Iosifescu, D. V., Castro, V. M., Murphy, S. N., Gainer, V. S., Minnier, J., Cai, T., Goryachev, S., Zeng, Q., Gallagher, P. J., et al. (2012) · Psychological Medicine · doi:10.1017/s0033291711000997 · Cited by 193 · cited 1× · see citing works
“Each community has a central node: Perlis et al [55] are cited in 7 other papers, Jackson et al [73] are cited in 4 other papers, Carrell et al [74] and Afshar et al [75] are cited in 2 other papers, and Bedi et al [39] are cited in 2 other papers.”
Digital diabetes: Perspectives for diabetes prevention, management and research
Fagherazzi, G., & Ravaud, P. (2018) · Diabetes & Metabolism · doi:10.1016/j.diabet.2018.08.012 · Cited by 191 · see citing works
Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid
Cook, B. L., Progovac, A. M., Chen, P., Mullin, B. O., Hou, S. S., & Baca‐García, E. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/8708434 · Cited by 176 · cited 2× · see citing works
“…found that ML and NLP techniques performed well, although they were not necessarily better than a practitioner’s ability to predict the clinical risk of suicide in their patients [61,62]; thus, the authors proposed statistical NLP approaches to be used in collaboration with clinical practice.”
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., & Sheth, A. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3123028 · Cited by 169 · cited 2× · see citing works
“For example, Roysden et al [56] screened administrative data and EHRs from a population of 12,759 patients; Maguen et al [63] compared over 8,168,330 clinical notes collected over 15 years; and Yazdavar et al [79] analyzed posts authored by 4000 Twitter users.”
Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing
Fernandes, A. C., Dutta, R., Velupillai, S. U., Sanyal, J., Stewart, R. J., & Chandran, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-25773-2 · Cited by 168 · cited 1× · see citing works
“Fernandes et al [60] studied EHRs from a cohort of individuals with a history of suicide attempts and a cohort of individuals with a history of suicidal ideation only.”
The Impact of Social Media on Medical Professionalism: A Systematic Qualitative Review of Challenges and Opportunities
Gholami-Kordkheili, F., Wild, V., & Strech, D. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2708 · Cited by 166 · cited 1× · see citing works
“Twitter and other social networks, with almost 3 billion users globally, have become significant sources of information for medical use and research [112].”
Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls
Castro, V. M., Minnier, J., Murphy, S. N., Kohane, I. S., Churchill, S. E., Gainer, V. S., Cai, T., Hoffnagle, A. G., Dai, Y. G., Block, S. R., et al. (2014) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.14030423 · Cited by 141 · cited 3× · see citing works
“All studies were thoroughly screened, and their main ideas are summarized in individual tables (Multimedia Appendix 2 [37-41, 43, 47,”
Influential
A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing
Tanana, M., Hallgren, K. A., Imel, Z. E., Atkins, D. C., & Srikumar, V. (2016) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2016.01.006 · Cited by 141 · cited 4× · see citing works
“All studies were thoroughly screened, and their main ideas are summarized in individual tables (Multimedia Appendix 2 [37-41, 43, 47,”
Influential
Measuring Use of Evidence Based Psychotherapy for Posttraumatic Stress Disorder
Shiner, B. R., DʼAvolio, L., Nguyen, T. M., Zayed, M. H., Young‐Xu, Y., Desai, R. A., Schnurr, P. P., Fiore, L. D., & Watts, B. V. (2012) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-012-0421-0 · Cited by 140 · cited 3× · see citing works
“…psychiatric diagnoses were addiction to alcohol or illicit drugs (6 cases) [43,65,66,75,84,86]; posttraumatic stress disorder (PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2…”
Influential
Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes
Patel, R., Lloyd, T., Jackson, R., Ball, M., Shetty, H., Broadbent, M. T. M., Geddes, J. R., Stewart, R. J., McGuire, P. K., & Taylor, M. J. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-007504 · Cited by 140 · cited 1× · see citing works
“…(PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85]; eating disorders (2 cases) [89,96]; and bipolar disorder (2 cases) [37,102].”
Artificial Intelligence in Behavioral and Mental Health Care
(2016) · Elsevier eBooks · doi:10.1016/c2013-0-12824-3 · Cited by 117 · see citing works
Analyzing big data in social media: Text and network analyses of an eating disorder forum
Moessner, M., Feldhege, J., Wolf, M., & Bauer, S. (2018) · International Journal of Eating Disorders · doi:10.1002/eat.22878 · Cited by 116 · cited 1× · see citing works
“…(PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85]; eating disorders (2 cases) [89,96]; and bipolar disorder (2 cases) [37,102].”
Using natural language processing to identify problem usage of prescription opioids
Carrell, D., Cronkite, D., Palmer, R. E., Saunders, K. W., Gross, D. E., Masters, E. T., Hylan, T. R., & Korff, M. V. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.09.002 · Cited by 114 · cited 2× · see citing works
“Each community has a central node: Perlis et al [55] are cited in 7 other papers, Jackson et al [73] are cited in 4 other papers, Carrell et al [74] and Afshar et al [75] are cited in 2 other papers, and Bedi et al [39] are cited in 2 other papers.”
Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study
Mowery, D. L., Smith, H. A., Cheney, T., Stoddard, G., Coppersmith, G., Bryan, C. J., & Conway, M. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6895 · Cited by 114 · cited 3× · see citing works
“By analyzing tweets, Mowery et al [106] found that there may be alternative ways in which people express depression.”
Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis
Kaur, P., & Sharma, M. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1341-2 · Cited by 114 · see citing works
Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records
Carson, N. J., Mullin, B. O., Román, M. J. S., Lu, F. Q., Yang, K. G., Menezes, M., & Cook, B. L. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211116 · Cited by 109 · cited 1× · see citing works
“Corpora can also be small, as demonstrated in the study conducted by Carson et al [38], who treated 73 respondents’ unstructured clinical notes, or in the study by Bedi et al [39], in which only 34 participants’ 1-hour-long narrative interviews were considered.”
Drug side effect extraction from clinical narratives of psychiatry and psychology patients
Sohn, S., Kocher, J., Chute, C. G., & Savova, G. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000351 · Cited by 108 · cited 1× · see citing works
“Treatment issues such as adherence or misuse are also depicted (6 cases) [56,72,74,81,101,103].”
Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method
Patel, R., Jayatilleke, N., Broadbent, M. T. M., Chang, C., Foskett, N., Gorrell, G., Hayes, R. D., Jackson, R., Johnston, C., Shetty, H., et al. (2015) · BMJ Open · doi:10.1136/bmjopen-2015-007619 · Cited by 107 · cited 1× · see citing works
“…(PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85]; eating disorders (2 cases) [89,96]; and bipolar disorder (2 cases) [37,102].”
Studying expressions of loneliness in individuals using twitter: an observational study
Guntuku, S. C., Schneider, R., Pelullo, A., Young, J. F., Wong, V., Ungar, L., Polsky, D. E., Volpp, K. G. M., & Merchant, R. M. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030355 · Cited by 106 · see citing works
Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks
Tran, T., & Kavuluru, R. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.06.010 · Cited by 101 · cited 3× · see citing works
“A total of 8 studies were transnosographic [59,67-71,”
Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation
Afshar, M., Phillips, A., Karnik, N., Mueller, J., To, D., Gonzalez, R., Price, R., Cooper, R., Joyce, C., & Dligach, D. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy166 · Cited by 94 · cited 2× · see citing works
“Each community has a central node: Perlis et al [55] are cited in 7 other papers, Jackson et al [73] are cited in 4 other papers, Carrell et al [74] and Afshar et al [75] are cited in 2 other papers, and Bedi et al [39] are cited in 2 other papers.”
What do we really know about blunted vocal affect and alogia? A meta-analysis of objective assessments
Cohen, A. S., Mitchell, K. R., & Elvevåg, B. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.09.013 · Cited by 91 · cited 2× · see citing works
“In addition, NLP is essential in psychiatry because language-based deficits are common symptoms of depression, behavioral disorder, ASD, personality disorder, and schizophrenia [30].”
A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department
Pestian, J. P., Grupp‐Phelan, J., Cohen, K. B., Meyers, G., Richey, L., Matykiewicz, P., & Sorter, M. T. (2015) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12180 · Cited by 81 · cited 2× · see citing works
“…found that ML and NLP techniques performed well, although they were not necessarily better than a practitioner’s ability to predict the clinical risk of suicide in their patients [61,62]; thus, the authors proposed statistical NLP approaches to be used in collaboration with clinical practice.”
Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media
Chary, M. A., Genes, N. G., Giraud-Carrier, C. G., Hanson, C. L., Nelson, L. S., & Manini, A. F. (2017) · Journal of Medical Toxicology · doi:10.1007/s13181-017-0625-5 · Cited by 75 · cited 2× · see citing works
“Treatment issues such as adherence or misuse are also depicted (6 cases) [56,72,74,81,101,103].”
Automated assessment of patients’ self-narratives for posttraumatic stress disorder screening using natural language processing and text mining
He, Q., Veldkamp, B. P., Glas, C. A. W., & de Vries, T. (2017) · Assessment · doi:10.1177/1073191115602551 · Cited by 74 · cited 4× · see citing works
“Data extraction was also used for diagnosis: He et al [47] diagnosed PTSD with an accuracy of 82% after analyzing free texts written by trauma survivors.”
Influential
Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting
Le, D. V., Montgomery, J., Kirkby, K. C., & Scanlan, J. D. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.007 · Cited by 73 · see citing works
Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales
Oh, J., Yun, K., Hwang, J.-H., & Chae, J. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00192 · Cited by 67 · see citing works
Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register
Iqbal, E., Mallah, R., Jackson, R., Ball, M., Ibrahim, Z. M., Broadbent, M. T. M., Dzahini, O., Stewart, R. J., Johnston, C., & Dobson, R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134208 · Cited by 65 · see citing works
Intimate insight: MDMA changes how people talk about significant others
Baggott, M. J., Kirkpatrick, M. G., Bedi, G., & de Wit, H. (2015) · Journal of Psychopharmacology · doi:10.1177/0269881115581962 · Cited by 65 · cited 2× · see citing works
“The following are some examples of studies that pursue this objective: Baggott et al [65] found that MDMA (3,4-méthylènedioxy-N-méthylamphétamine; Ecstasy) altered individuals’ speech patterns more frequently than the placebo and led to an increase in both positive and negative social and sexual…”
Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study
Metzger, M.-H., Tvardik, N., Gicquel, Q., Bouvry, C., Poulet, E., & Potinet, V. (2016) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1522 · Cited by 64 · cited 2× · see citing works
“A total of 2 studies targeted automated epidemiological monitoring: Metzger et al [57] provided a method of detecting suicide attempts from EHRs and Leroy et al [58] achieved automatic extraction of criteria for ASD from EHRs with an accuracy of 76%.”
Influential
Measuring Use of Evidence Based Psychotherapy for Posttraumatic Stress Disorder in a Large National Healthcare System
Maguen, S., Madden, E., Patterson, O. V., DuVall, S. L., Goldstein, L. A., Burkman, K. M., & Shiner, B. R. (2018) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-018-0850-5 · Cited by 64 · see citing works
Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations
Hoogendoorn, M., Berger, T., Schulz, A., Stolz, T., & Peter Szolovits, B. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2601123 · Cited by 60 · cited 1× · see citing works
“…(PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85]; eating disorders (2 cases) [89,96]; and bipolar disorder (2 cases) [37,102].”
Predicting inadequate postoperative pain management in depressed patients: A machine learning approach
Parthipan, A., Banerjee, I., Humphreys, K. N., Asch, S. M., Curtin, C. M., Carroll, I. R., & Hernandez‐Boussard, T. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210575 · Cited by 60 · see citing works
Clustering algorithms for bank customer segmentation
Zakrzewska, D., & Murlewski, J. (2005) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2005.33 · Cited by 59 · see citing works
Protecting Life While Preserving Liberty: Ethical Recommendations for Suicide Prevention With Artificial Intelligence
McKernan, L. C., Clayton, E. W., & Walsh, C. G. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00650 · Cited by 52 · see citing works
Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem
Zhong, Q., Mittal, L. P., Nathan, M., Brown, K. M., Gonzalez, D. K., Cai, T. A., Finan, S., Gelaye, B., Avillach, P., Smoller, J. W., et al. (2018) · European Journal of Epidemiology · doi:10.1007/s10654-018-0470-0 · Cited by 51 · cited 1× · see citing works
“Zhong et al [78] have the highest degree: this paper has more than three common references with as many as eight other papers, in fact, with 8 references.”
A robust voting approach for diabetes prediction using traditional machine learning techniques
Mahabub, A. (2019) · SN Applied Sciences · doi:10.1007/s42452-019-1759-7 · Cited by 51 · cited 1× · see citing works
“…disease models in cardiology, neurology, and diabetes research [6] to automatically identify heart disease risk factors [7], to classify primary progressive aphasia subtypes [8], and for the characterization and diagnosis of cognitive impairments [9], diabetes, and cardiovascular disorders [10-17].”
Suicide Among Older Adults Living in or Transitioning to Residential Long-term Care, 2003 to 2015
Mezuk, B. R., Ko, T. M., Kalesnikava, V. A., & Jurgens, D. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.5627 · Cited by 46 · see citing works
Automatic detection of eating disorder‐related social media posts that could benefit from a mental health intervention
Yan, H., Fitzsimmons‐Craft, E. E., Goodman, M., Krauss, M. J., Das, S., & Cavazos‐Rehg, P. A. (2019) · International Journal of Eating Disorders · doi:10.1002/eat.23148 · Cited by 45 · cited 1× · see citing works
“…(PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85]; eating disorders (2 cases) [89,96]; and bipolar disorder (2 cases) [37,102].”
TextHunter - A User Friendly Tool for Extracting Generic Concepts from Free Text in Clinical Research
Jackson, R., Ball, M., Patel, R., Hayes, R., Dobson, R., & Stewart, R. J. (2014) · PubMed Central · doi:10.13140/2.1.3722.9121 · Cited by 44 · see citing works
Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application
Gondy Leroy, A., Gu, Y., Pettygrove, S. D., Galindo, M. K., Arora, A., & Kurzius‐Spencer, M. (2018) · Journal of Medical Internet Research · doi:10.2196/10497 · Cited by 43 · cited 2× · see citing works
“…or illicit drugs (6 cases) [43,65,66,75,84,86]; posttraumatic stress disorder (PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85];…”
Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2
Filannino, M., Stubbs, A., & Uzuner, Ö. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.04.017 · Cited by 42 · cited 2× · see citing works
“A total of 8 studies were transnosographic [59,67-71,”
An automatic system to identify heart disease risk factors in clinical texts over time
Chen, Q., Li, H., Tang, B., Wang, X., Liu, X., Liu, Z., Liu, S., Wang, W., Deng, Q., Zhu, S., et al. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.09.002 · Cited by 41 · cited 1× · see citing works
“…is widely used in critical disease models in cardiology, neurology, and diabetes research [6] to automatically identify heart disease risk factors [7], to classify primary progressive aphasia subtypes [8], and for the characterization and diagnosis of cognitive impairments [9], diabetes, and…”
Machine Learning Will Change Medicine
Forsting, M. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.190397 · Cited by 41 · cited 1× · see citing works
“Forsting et al [45] acknowledge that ML and NLP techniques can be useful for optimism bias (eg, the difference between a person’s expectation and the actual outcome or the concept that a clinician may think that his or her patient’s problem falls solely into a specific discipline in which the…”
Unraveling the linguistic nature of specific autobiographical memories using a computerized classification algorithm
Takano, K., Ueno, M., Moriya, J., Mori, M., Nishiguchi, Y., & Raes, F. (2016) · Behavior Research Methods · doi:10.3758/s13428-016-0753-x · Cited by 40 · cited 1× · see citing works
“Only 1 study on mechanical restraints [90] and 1 on cognitive troubles [97] were found.”
The rate of reply and nature of responses to suicide-related posts on Twitter
O’Dea, B., Achilles, M. R., Larsen, M. E., Batterham, P. J., Calear, A. L., & Christensen, H. M. (2018) · Internet Interventions · doi:10.1016/j.invent.2018.07.004 · Cited by 40 · cited 1× · see citing works
“Through analyses of Twitter posts, O’Dea et al [49] identified the importance of creating real-time campaigns to increase help-seeking behaviors and reduce the stigma attached to mental health.”
Machine Learning and Evidence-Based Medicine
Scott, I. A. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0115 · Cited by 39 · cited 2× · see citing works
“…traditional epidemiologic approach of evidence-based medicine owing to its high processing speed and ability to handle large volumes of data with heterogeneous variables (electronic health records, administrative data sets, wearable sensors, genomic and proteomic databanks, and social media) [18].”
Clustering Suicide Attempters: Impulsive-Ambivalent, Well-Planned, or Frequent
Lopez-Castroman, J., Nogue, E., Guillaume, S., Picot, M. C., & Courtet, P. (2016) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.15m09882 · Cited by 38 · cited 1× · see citing works
“From a mental health perspective, the prevention of suicidal risk has recently been substantially studied [23-26].”
Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data
Danielsen, A. A., Fenger, M. H. J., Østergaard, S. D., Nielbo, K. L., & Mors, O. (2019) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13061 · Cited by 38 · cited 1× · see citing works
“Only 1 study on mechanical restraints [90] and 1 on cognitive troubles [97] were found.”
Is it ethical to use prognostic estimates from machine learning to treat psychosis
(2018) · AMA Journal of Ethics · doi:10.1001/amajethics.2018.804 · Cited by 33 · see citing works
Analyzing depression tendency of web posts using an event-driven depression tendency warning model
Tung, C.-M., & Lu, W. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.10.003 · Cited by 32 · see citing works
Candyflipping and Other Combinations: Identifying Drug–Drug Combinations from an Online Forum
Chary, M. A., Yi, D., & Manini, A. F. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00135 · Cited by 32 · cited 4× · see citing works
“In addition, when Chary et al [66] focused on Lycaeum users and Coppersmith et al [76] mentioned participants from a company , the lack of precise information on the participants of a cohort was obvious.”
Influential
Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data
Topaz, M., Murga, L., Bar‐Bachar, O., Cato, K., & Collins, S. (2019) · Studies in health technology and informatics · doi:10.3233/shti190386 · Cited by 32 · cited 1× · see citing works
“Other psychiatric diagnoses were addiction to alcohol or illicit drugs (6 cases) [43,65,66,75,84,86]; posttraumatic stress disorder (PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2…”
The Research Domain Criteria (RDoC) and the historical roots of psychopathology: A viewpoint
Stoyanov, D., Telles‐Correia, D., & Cuthbert, B. N. (2019) · European Psychiatry · doi:10.1016/j.eurpsy.2018.11.007 · Cited by 29 · cited 1× · see citing works
“Pro-RDoC practitioners argue that DSM syndromes have significant limitations when used as phenotypes for identifying biomarkers and specific genetic variants associated with mental illness [108].”
Scalable mental health analysis in the clinical whitespace via natural language processing
Coppersmith, G., Hilland, C., Frieder, O., & Leary, R. (2017) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2017.7897288 · Cited by 28 · cited 3× · see citing works
“In addition, when Chary et al [66] focused on Lycaeum users and Coppersmith et al [76] mentioned participants from a company , the lack of precise information on the participants of a cohort was obvious.”
How Do You #relax When You’re #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets
Doan, S., Ritchart, A., Perry, N., Chaparro, J. D., & Conway, M. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.5939 · Cited by 28 · cited 7× · see citing works
“Typical examples are in studies by Doan et al [41] and Jackson et al [73], in which tweets were selected because they contained the hashtags #stress and #relax and their words were used in a bag-of-words without any further linguistic treatment [41] or tweets were selected based on the presence of…”
Influential
Validation of an alcohol misuse classifier in hospitalized patients
To, D., Sharma, B., Karnik, N. S., Joyce, C., Dligach, D., & Afshar, M. (2019) · Alcohol · doi:10.1016/j.alcohol.2019.09.008 · Cited by 27 · see citing works
Advances in Decision Sciences, Image Processing, Security and Computer Vision
(2019) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-24322-7 · Cited by 26 · see citing works
Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports
Scheurwegs, E., Sushil, M., Tulkens, S., Daelemans, W. M. P., & Luyckx, K. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.06.007 · Cited by 24 · see citing works
Text Classification to Inform Suicide Risk Assessment in Electronic Health Records
Bittar, A., Velupillai, S. U., Roberts, A., & Dutta, R. (2019) · Studies in health technology and informatics · doi:10.3233/shti190179 · Cited by 24 · see citing works
Mining association language patterns using a distributional semantic model for negative life event classification
Yu, L.-C., Chan, C., Lin, C.-C., & Lin, I.-C. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.01.006 · Cited by 23 · see citing works
An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support
Berrouiguet, S., Billot, R., Larsen, M. E., López‐Castromán, J., Jaussent, I., Walter, M., Lenca, P., Baca‐García, E., & Courtet, P. (2018) · JMIR Mental Health · doi:10.2196/mental.9766 · Cited by 21 · see citing works
Identifying epilepsy psychiatric comorbidities with machine learning
Glauser, T., Santel, D., Delbello, M., Faist, R., Toon, T., Clark, P. O., McCourt, R., Wissel, B. D., & Pestian, J. (2019) · Acta Neurologica Scandinavica · doi:10.1111/ane.13216 · Cited by 21 · see citing works
Automatic recognition of symptom severity from psychiatric evaluation records
Goodwin, T. R., Maldonado, R., & Harabagiu, S. M. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.05.020 · Cited by 20 · cited 3× · see citing works
“Goodwin et al [59] classified symptoms of patients with psychosis into 4 different levels of severity (absent, mild, moderate, and severe) using statistical analyses.”
Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records
Barroilhet, S. A., Pellegrini, A. M., McCoy, T. H., & Perlis, R. H. (2019) · Psychological Medicine · doi:10.1017/s0033291719002320 · Cited by 19 · cited 1× · see citing works
“…(PTSD; 3 cases) [47,63,64]; neurodevelopmental disorders (3 cases) [42,58,93]; psychotic spectrum disorders, including schizophrenia (3 cases) [39,95,100]; anxiety (2 cases) [41,98]; personality disorder (1 case) [85]; eating disorders (2 cases) [89,96]; and bipolar disorder (2 cases) [37,102].”
Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting
Paz, E. V., Martino, M., Hyland, M., Corleto, M., Puga, C., Peralta, M., Deltetto, N., Kuhlmann, T., Cavalié, D., Leist, M., et al. (2019) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-019-04242-3 · Cited by 18 · see citing works
Semantic mapping reveals distinct patterns in descriptions of social relations in adults with autism spectrum disorder
Luo, S. X., Shinall, J. A., Peterson, B. S., & Gerber, A. J. (2015) · Autism Research · doi:10.1002/aur.1581 · Cited by 16 · cited 4× · see citing works
“However, in practice, the selected studies tend to confirm clinical hypotheses based on fundamental clinical intuitions, namely language abnormalities in adults with ASD [42].”
Influential
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
Sharma, M., Singh, G., & Singh, R. (2018) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.19-6-2018.154828 · Cited by 16 · see citing works
Investigación traslacional en psiquiatría: el marco Research Domain Criteria (RDoC)
Vilar, A., Pérez, V., Blasco, M. J., Pérez-Gallo, E., Ballester, L., Vila, S., Alonso, J., Serrano‐Blanco, A., & Forero, C. G. (2018) · Revista de Psiquiatría y Salud Mental · doi:10.1016/j.rpsm.2018.04.002 · Cited by 16 · see citing works
Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records
Posada, J., Barda, A. J., Shi, L., Xue, D., Ruiz, V., Kuan, P.-H., Ryan, N. D., & Tsui, F. R. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.05.019 · Cited by 15 · see citing works
Translational research in psychiatry: The Research Domain Criteria Project (RDoC)
Vilar, A., Pérez, V., Blasco, M. J., Pérez-Gallo, E., Ballester, L., Vila, S., Alonso, J., Serrano‐Blanco, A., & Forero, C. G. (2019) · Revista de Psiquiatría y Salud Mental (English Edition) · doi:10.1016/j.rpsmen.2018.04.002 · Cited by 15 · see citing works
Unveiling Online Suicide Behavior: What Can We Learn About Mental Health from Suicide Survivors of Reddit?
Karthik, A. A., Moulahi, B., Azé, J., & Bringay, S. (2019) · Studies in health technology and informatics · doi:10.3233/shti190181 · Cited by 15 · see citing works
Automatic classification of RDoC positive valence severity with a neural network
Clark, C., Wellner, B., Davis, R., Aberdeen, J. S., & Hirschman, L. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.07.005 · Cited by 13 · see citing works
What do patients learn about psychotropic medications on the web? A natural language processing study
Hart, K. L., Perlis, R. H., & McCoy, T. H. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.09.043 · Cited by 12 · cited 1× · see citing works
“Treatment issues such as adherence or misuse are also depicted (6 cases) [56,72,74,81,101,103].”
Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients
Dai, H.-J., Su, E. C., Uddin, M., Jonnagaddala, J., Wu, C., & Syed-Abdul, S. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.08.009 · Cited by 10 · cited 2× · see citing works
“Similarly, studies rarely consider cultural or ethnic differences within a sample [80].”
A methodology for the characterization and diagnosis of cognitive impairments—Application to specific language impairment
Oliva, J., Serrano, J. I., del Castillo, M. D., & Iglesias, Á. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.04.002 · Cited by 9 · cited 1× · see citing works
“…disease models in cardiology, neurology, and diabetes research [6] to automatically identify heart disease risk factors [7], to classify primary progressive aphasia subtypes [8], and for the characterization and diagnosis of cognitive impairments [9], diabetes, and cardiovascular disorders [10-17].”
Analyzing Abusive Text Messages to Detect Digital Dating Abuse
Roy, T., McClendon, J. L., & Hodges, L. F. (2018) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2018.00039 · Cited by 9 · cited 3× · see citing works
“As stated in the Results section, one of the 3 main population categories is social network or chat users [40,41,66,77,79], whose members are predominantly young.”
Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.
Roysden, N., & Wright, A. T. (2015) · PubMed · Cited by 7 · see citing works
Défense de la clinique en psychiatrie
(2014) · Érès eBooks · doi:10.3917/eres.sasso.2014.01 · Cited by 5 · see citing works
Towards Constructing a New Taxonomy for Psychiatry Using Self-reported Symptoms
Ross, J. A., Neylan, T. C., Weiner, M. W., Linda, C., Samuelson, K. W., & Sim, I. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-736 · Cited by 2 · cited 1× · see citing works
“A concrete application of this new system used statistical NLP methods to create a phenotypic homogenous cohort that allowed a better comparison [109].”

Flagged first, then by citations

Research works printed but not linked: 4

[15]Gauthier E Brisson L Lenca P Ragusa S Breast Cancer Risk Score: a Data Mining Approach to Improve Readability The International Conference on Data Mining 2011 CDM'11 June 23-27, 2011 Las Vegas, United States 15 21
[16]Gauthier E Brisson L Lenca P Clavel-Chapelon F Ragusa S Challenges to Building a Platform for a Breast Cancer Risk Score Sixth International Conference on Research Challenges in Information Science 2012 5 RCIS'12 June 1-7, 2012 Valencia, Spain
[31]Hays DG Introduction to Computational Linguistics, Mathematical Linguistics and Automatic Language Processing 1967 Cambridge American Elsevier Publishing Co
[44]Haralambous Y Lemey C Lenca P Billot R Kim-Dufor DH Using NLP Methods to Predict Psychiatric Comorbidities the RaPID-3: Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive, psychiatric and/or developmental impairments Workshop 2020 RAPID'20 May 2-6, 2020 Marseille

Sorted by citations

The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research
Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Al Yami, M. S., et al. (2023) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2023.05.016 · Cited by 565 · see citing works
Machine learning-based approach: global trends, research directions, and regulatory standpoints
Pugliese, R., Regondi, S., & Marini, R. (2021) · Data Science and Management · doi:10.1016/j.dsm.2021.12.002 · Cited by 394 · see citing works
Large Language Models for Mental Health Applications: Systematic Review
Guo, Z., Lai, A., Thygesen, J. H., Farrington, J., Keen, T., & Li, K. (2024) · JMIR Mental Health · doi:10.2196/57400 · Cited by 269 · see citing works
“The existing literature includes a review of the application of machine learning and NLP in mental health [47], analyses of LLMs in medicine [32], and a scoping review of LLMs in mental health.”
BackgroundMethod
A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health
Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916221134490 · Cited by 263 · see citing works
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 225 · see citing works
“Language is not only one of the primary expressions of human behavior that carries a variety of implicit and explicit markers relevant to mental health [193, 194], but also more abundantly available compared to speech data.”
Background
Benefits and Risks of AI in Health Care: Narrative Review
Chustecki, M. (2024) · Interactive Journal of Medical Research · doi:10.2196/53616 · Cited by 208 · see citing works
“Le Glaz et al [32] mentions that in recent years, NLP models have been used to track mental self-disclosure on Twitter, forecast suicide risk online, and identify suicidal thoughts in clinical notes.”
A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom
Shaik, T., Tao, X., Li, L., Xie, H., & Velasquez, J. D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102040 · Cited by 213 · see citing works
“NLP techniques contribute to patient risk assessment by analyzing clinical narratives and extracting relevant information related to patient history, comorbidities, and lifestyle factors [127].”
Background
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches
Khoo, L. S., McNaney, R., Lim, M. K., & McNaney, R. (2024) · Sensors · doi:10.3390/s24020348 · Cited by 178 · see citing works
Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness
Oyebode, O., Fowles, J. R., Steeves, D., & Orji, R. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2089085 · Cited by 155 · see citing works
“…as disease management (Martin-Isla et al., 2020; Mlodzinski et al., 2020; Thomsen et al., 2020; Zhu et al., 2021), health surveillance (Gupta & Katarya, 2020), mental health (Cho et al., 2019; Le Glaz et al., 2021; Shatte et al., 2019), and other health-related issues (e.g., Qian et al., 2021).”
Background
Digital accessibility in the era of artificial intelligence—Bibliometric analysis and systematic review
Chemnad, K., & Othman, A. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1349668 · Cited by 140 · see citing works
Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review
Abayomi‐Alli, O., Damaševičius, R., Qazi, A., Adedoyin-Olowe, M., & Misra, S. (2022) · Electronics · doi:10.3390/electronics11223795 · Cited by 137 · see citing works
Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model
Dergaa, I., Saad, H. B., Omri, A. E., Glenn, J. M., Clark, C. C. T., Washif, J. A., Guelmami, N., Hammouda, O., Al-Horani, R., Reynoso-Sánchez, L. F., et al. (2023) · Biology of Sport · doi:10.5114/biolsport.2024.133661 · Cited by 133 · see citing works
“including nutrition [18], mental health [19, 20], and fitness [4, 21].”
Background
Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions – A Narrative Review for a Comprehensive Insight
Alhuwaydi, A. M. (2024) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s461562 · Cited by 152 · see citing works
Artificial intelligence, machine learning, and deep learning for clinical outcome prediction
Pettit, R. W., Fullem, R. L., Cheng, C., & Amos, C. I. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210246 · Cited by 137 · see citing works
Artificial intelligence and suicide prevention: A systematic review
Lejeune, A., Glaz, A. L., Perron, P.-A., Sebti, J., Baca‐García, E., Walter, M., Lemey, C., & Berrouiguet, S. (2022) · European Psychiatry · doi:10.1192/j.eurpsy.2022.8 · Cited by 123 · see citing works
“AI raises ethical concerns within the medical community [44,45], especially regarding the use of data, the place of the practitioner in care, and ethical and medico-legal responsibility.”
Background
Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review
Arowosegbe, A., & Oyelade, T. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021514 · Cited by 101 · see citing works
“This research validates the results of a previous systematic reviews by [56], which concluded that NLP can be used in detecting and treating mental health issues including suicide and self-harm.”
Result
Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison
Hossain, M. I., Maruf, M. H., Khan, M. A. R., Prity, F. S., Fatema, S., Ejaz, M. S., & Khan, M. A. S. (2023) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00148-7 · Cited by 91 · see citing works
“Inmanymedical applications, such as medical image analysis [7–10], language processing [11, 12], and tumor or cancer cell detection [13–17], ML methods can assist clinical management, and professionals explore excellent performance.”
Background
Physics-informed neural network for bending and free vibration analysis of three-dimensional functionally graded porous beam resting on elastic foundation
Fallah, A., & Aghdam, M. M. (2023) · Engineering With Computers · doi:10.1007/s00366-023-01799-7 · Cited by 86 · see citing works
Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies
An, R., Shen, J., & Xiao, Y. (2022) · Journal of Medical Internet Research · doi:10.2196/40589 · Cited by 86 · see citing works
“Early NLP models primarily adopted recurrent neural network (RNN) architecture, demonstrating broad applicability to various NLP tasks such as sentiment analysis, text summarization, language translation, and speech recognition [74,132].”
Systematic reviews of machine learning in healthcare: a literature review
Kolasa, K., Admassu, B. Y., Hołownia-Voloskova, M. E., Kędzior, K. J., Poirrier, J., & Perni, S. (2023) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2023.2279107 · Cited by 87 · see citing works
Opportunities, applications, challenges and ethical implications of artificial intelligence in psychiatry: a narrative review
Terra, M., Baklola, M. I., Ali, S. A. A., & El-Bastawisy, K. (2023) · The Egyptian Journal of Neurology Psychiatry and Neurosurgery · doi:10.1186/s41983-023-00681-z · Cited by 77 · see citing works
Large Language Models in Neurology Research and Future Practice
Romano, M. F., Shih, L. C., Paschalidis, I. C., Au, R., & Kolachalama, V. B. (2023) · Neurology · doi:10.1212/wnl.0000000000207967 · Cited by 73 · see citing works
Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses
BAYDİLİ, I., Taşçı, B., & Taşçı, G. (2025) · Diagnostics · doi:10.3390/diagnostics15040434 · Cited by 85 · see citing works
Cheap, Quick, and Rigorous: Artificial Intelligence and the Systematic Literature Review
Atkinson, C. F. (2023) · Social Science Computer Review · doi:10.1177/08944393231196281 · Cited by 73 · see citing works
“If data is input that is noisy, incomplete, or has been extracted through processes that have introduced selective biases, then the results themselves may be erroneous (Le Glaz et al., 2021).”
BackgroundMethod
Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews
Martínez-Millana, A., Saez-Saez, A., Tornero-Costa, R., Muscat, N. A., Traver, V., & Novillo-Ortiz, D. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104855 · Cited by 73 · see citing works
The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review
Zafar, F., Alam, L. F., Vivas, R. R., Wang, J., Whei, S. J., Mehmood, S., Sadeghzadegan, A., Lakkimsetti, M., & Nazir, Z. (2024) · Cureus · doi:10.7759/cureus.56472 · Cited by 74 · see citing works
“Precision medicine, which predicts treatment response and personalizes therapeutic interventions for individuals, is another potential application of the former [52].”
Background
Natural language processing in clinical neuroscience and psychiatry: A review
Crema, C., Attardi, G., Sartiano, D., & Redolfi, A. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.946387 · Cited by 65 · see citing works
“Other reviews do not present the breakthroughs of the last 4–5 years (attention mechanism, transformers, BERT-models, etc.), either because they are relatively old (11–13), or because they present the current state-of-the-art at a high level (14–17).”
Background
Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks
Pourkeyvan, A., Safa, R., & Sorourkhah, A. (2024) · IEEE Access · doi:10.1109/access.2024.3366653 · Cited by 68 · see citing works
“Automated systems for detecting mental health issues have been developed to analyze textual data, with NLP and transformer models being utilized to identify patterns [7].”
BackgroundMethod
Intellectual Disability and Technology: An Artificial Intelligence Perspective and Framework
Almufareh, M. F., Tehsin, S., Humayun, M., & Kausar, S. (2023) · Journal of Disability Research · doi:10.57197/jdr-2023-0055 · Cited by 57 · see citing works
“This can help to identify symptoms, history or distinct markers associated with ID (de la Fuente Garcia et al., 2020; Le Glaz et al., 2021; Ratana et al., 2023).”
Background
Natural Language Processing: from Bedside to Everywhere
Aramaki, E., Wakamiya, S., Yada, S., & Nakamura, Y. (2022) · Yearbook of Medical Informatics · doi:10.1055/s-0042-1742510 · Cited by 52 · see citing works
“In psychiatry, NLP can be used for IE from unstructured EHR and speech analysis on patient speech data [63, 64].”
Extracting cancer concepts from clinical notes using natural language processing: a systematic review
Gholipour, M., Khajouei, R., Amiri, P., Hajesmaeel‐Gohari, S., & Ahmadian, L. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05480-0 · Cited by 52 · see citing works
“Sheikhalishahi et al. [28] carried out a comprehensive overview of the development and uptake of NLP methods in open-text clinical notes related to chronic diseases, including an examination of the challenges of using NLP methods to extract terms from clinical narratives.”
Method
Screening for Depression Using Natural Language Processing: Literature Review
Teferra, B. G., Rueda, A., Pang, H. Y. M., Valenzano, R., Samavi, R., Krishnan, S., & Bhat, V. (2024) · Interactive Journal of Medical Research · doi:10.2196/55067 · Cited by 51 · see citing works
Textual emotion detection in health: Advances and applications
Saffar, A. H., Mann, T. K., & Ofoghi, B. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104258 · Cited by 48 · see citing works
AI in Mental Health: A Review of Technological Advancements and Ethical Issues in Psychiatry
Poudel, U., Jakhar, S., Prakash, M., & Nepal, A. (2025) · Issues in Mental Health Nursing · doi:10.1080/01612840.2025.2502943 · Cited by 46 · see citing works
AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling
Nedungadi, P., Veena, G., Tang, K., Menon, R. R. K., & Raghu Raman, R. (2025) · IEEE Access · doi:10.1109/access.2025.3543795 · Cited by 51 · see citing works
“Maintaining privacy while leveraging social media data for health monitoring is crucial to protecting user rights and building trust in AI systems [84], [88].”
Machine learning applied to electronic health record data in home healthcare: A scoping review
Hobensack, M., Song, J., Scharp, D., Bowles, K. H., & Topaz, M. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104978 · Cited by 44 · see citing works
Methodologies for Monitoring Mental Health on Twitter: Systematic Review
Cara, N. H. D., Maggio, V., Davis, O. S. P., & Haworth, C. M. A. (2023) · Journal of Medical Internet Research · doi:10.2196/42734 · Cited by 46 · see citing works
Harnessing AI in Anxiety Management: A Chatbot-Based Intervention for Personalized Mental Health Support
Manole, A., Cârciumaru, R., Brînzaș, R., & Manole, F. (2024) · Information · doi:10.3390/info15120768 · Cited by 52 · see citing works
AI in Healthcare: medical and socio-economic benefits and challengesPreprint
Shaheen, M. Y. (2021) · doi:10.14293/s2199-1006.1.sor-.pprqni1.v1 · Cited by 43 · see citing works
“NLP models have been used to detect suicidal thoughts in clinical notes, forecast suicide risk online, and mine for psychiatric self-disclosure on Twitter in recent years (Le Glaz et al., 2021).”
Method
Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review
Holmes, G., Tang, B., Gupta, S., Venkatesh, S., Christensen, H. M., & Whitton, A. E. (2024) · Journal of Medical Internet Research · doi:10.2196/63126 · Cited by 42 · see citing works
Natural Language Processing Methods and Bipolar Disorder: Scoping Review
Harvey, D., Lobban, F. A., Rayson, P., Warner, A., & Jones, S. H. (2022) · JMIR Mental Health · doi:10.2196/35928 · Cited by 40 · see citing works
Detecting Language Associated With Home Healthcare Patient’s Risk for Hospitalization and Emergency Department Visit
Song, J., Ojo, M., Bowles, K. H., McDonald, M. V., Cato, K., Rossetti, S. C., Adams, V., Chae, S., Hobensack, M., Kennedy, E. E., et al. (2022) · Nursing Research · doi:10.1097/nnr.0000000000000586 · Cited by 40 · see citing works
“Several recent systematic reviews support the influence of NLP on a diverse range of medical fields, such as mental health (Le Glaz et al., 2021) and radiology (Sorin et al., 2020).”
Background
Diagnostic and Management Performance of ChatGPT in Obstetrics and Gynecology
Allahqoli, L., Ghiasvand, M. M., Mazidimoradi, A., Salehiniya, H., & Alkatout, İ. (2023) · Gynecologic and Obstetric Investigation · doi:10.1159/000533177 · Cited by 40 · see citing works
“A new approach to NLP based on statistical methods emerged in the 1990s, when personal computers became widely accessible and more powerful [7].”
Method
Challenges and standardisation strategies for sensor-based data collection for digital phenotyping
Alam, N. B., Surani, M., Das, C. K., Giacco, D., Singh, S. P., & Jilka, S. (2025) · Communications Medicine · doi:10.1038/s43856-025-01013-3 · Cited by 41 · see citing works
A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges
Bhattacharya, M., Roy, S., Chattopadhyay, S., Das, A. K., & Shetty, S. S. (2022) · Security and Privacy · doi:10.1002/spy2.275 · Cited by 39 · see citing works
RETRACTED ARTICLE: IoT in healthcare: a review of services, applications, key technologies, security concerns, and emerging trends
Nissar, G., Khan, R. A., Mushtaq, S., Lone, S. A., & Moon, A. H. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18580-7 · Cited by 40 · see citing works
Retracted
Federated learning meets remote sensing
Moreno‐Álvarez, S., Paoletti, M. E., Sanchez‐Fernandez, A. J., Rico‐Gallego, J., Han, L., & Haut, J. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124583 · Cited by 41 · see citing works
Natural language processing for clinical notes in dentistry: A systematic review
Pethani, F., & Dunn, A. G. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104282 · Cited by 37 · see citing works
Analyzing Narratives of Patient Experiences: A BERT Topic Modeling Approach
Osváth, M., Yang, Z. G., & Kósa, K. (2023) · Acta Polytechnica Hungarica · doi:10.12700/aph.20.7.2023.7.9 · Cited by 37 · see citing works
The Role of Artificial Intelligence in Mental Health: A Review
Shimada, K. (2023) · Science Insights · doi:10.15354/si.23.re820 · Cited by 41 · see citing works
Large Language Models and Text Embeddings for Detecting Depression and Suicide in Patient Narratives
Lho, S. K., Park, S. C., Lee, H., Oh, D. Y., Kim, H., Jang, S., Jung, H. Y., Yoo, S. Y., Park, S. M., & Lee, J. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.11922 · Cited by 36 · see citing works
Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation
Cohen, A. S., Rodriguez, Z. B., Warren, K. K., Cowan, T., Masucci, M. D., Granrud, O. E., Holmlund, T. B., Chandler, C., Foltz, P. W., & Strauss, G. P. (2022) · Schizophrenia Bulletin · doi:10.1093/schbul/sbac051 · Cited by 36 · see citing works
Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review
Yee, T. S., Shrifan, N. H. M. M., Al-Gburi, A. J. A., Isa, N. A. M., & Akbar, M. F. (2022) · IEEE Access · doi:10.1109/access.2022.3197291 · Cited by 36 · see citing works
“real-world applications such as biomedical [34]–[37], image processing [38]–[40], and language processing [41]–[43].”
Background
Detecting depression tendency with multimodal features
Zhang, H., Wang, H., Han, S. Y., Li, W., & Zhuang, L. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107702 · Cited by 34 · see citing works
Use of Artificial Intelligence in Adolescents’ Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions
Sharma, G., Yaffe⃰, M. J., Ghadiri, P., Gandhi, R., Pinkham, L., Gore, G. C., & Rahimi, S. A. (2025) · JMIR Mental Health · doi:10.2196/70438 · Cited by 43 · see citing works
Optimizing Chatbot Effectiveness through Advanced Syntactic Analysis: A Comprehensive Study in Natural Language Processing
Ortiz-Garcés, I., Govea, J., Andrade, R., & Villegas-Ch, W. E. (2024) · Applied Sciences · doi:10.3390/app14051737 · Cited by 33 · see citing works
Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis
Villarreal‐Zegarra, D., Reátegui-Rivera, C. M., García-Serna, J., Quispe-Callo, G., Lázaro-Cruz, G., Centeno-Terrazas, G., Gálvez-Arévalo, R. A., Escobar-Agreda, S., Rodríguez, A. D., & Finkelstein, J. (2024) · JMIR Mental Health · doi:10.2196/59560 · Cited by 36 · see citing works
“Previously, other systematic reviews, such as those conducted by Le Glaz et al [19] and Zhang et al [20], analyzed the impact of NLP on mental health.”
Method
Evaluating Large Language Models in extracting cognitive exam dates and scores
Zhang, H., Jethani, N., Jones, S., Genes, N. G., Major, V. J., Jaffe, I. S., Cardillo, A. B., Heilenbach, N., Ali, N. F., Bonanni, L. J., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000685 · Cited by 32 · see citing works
“Information extraction involves the retrieval of specific bits of information from unstructured clinical notes, a task historically handled by rule-based systems [24,25] [26] [27] [28] [29] [30] or language models explicitly trained on datasets annotated by human experts [31] [32] [33] [34] [35] [36].”
Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study
Li, T. M. H., Chen, J., Law, F., Li, C.-T., Chan, N. Y., Chan, J. W. Y., Chau, S. W. H., Liu, Y., Li, S. X., Zhang, J., et al. (2023) · JMIR Medical Informatics · doi:10.2196/50221 · Cited by 31 · see citing works
“Natural language processing (NLP) and machine learning (ML) have been used to detect suicide risk [18,19].”
BackgroundMethod
Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review
Sim, J.-A., Huang, X., Horan, M. R., Baker, J. N., & Huang, I. (2024) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2024.2322664 · Cited by 35 · see citing works
The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study
Jung, S., Murthy, D., Bataineh, B. S., Loukas, A., & Wilkinson, A. V. (2024) · Journal of Medical Internet Research · doi:10.2196/55591 · Cited by 31 · see citing works
“Natural language processing techniques are instrumental in mining textual data from web-based health forums, social media platforms, and patient-generated content [41].”
Explainable artificial intelligence systems for predicting mental health problems in autistics
Atlam, E.-S., Rokaya, M., Masud, M., Meshref, H., Alotaibi, R. S., Almars, A. M., Assiri, M., & Gad, I. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.12.120 · Cited by 31 · see citing works
Using Large Language Models to Understand Suicidality in a Social Media–Based Taxonomy of Mental Health Disorders: Linguistic Analysis of Reddit Posts
Bauer, B. W., Norel, R., Leow, A., Rached, Z. A., Wen, B., & Cecchi, G. (2024) · JMIR Mental Health · doi:10.2196/57234 · Cited by 30 · see citing works
“The use of natural language processing (NLP) with machine learning (ML) to gain new insights into mental health topics has increased dramatically over the last decade [10].”
BackgroundMethod
Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model
Mezzi, R., Yahyaoui, A., Krir, M. W., Boulila, W., & Koubâa, A. (2022) · Sensors · doi:10.3390/s22030846 · Cited by 29 · see citing works
An effective approach for early liver disease prediction and sensitivity analysis
Khan, M. A. R., Afrin, F., Prity, F. S., Ahammad, I., Fatema, S., Prosad, R., Hasan, M. K., Uddi̇n, M., & Salehin, Z. U. (2023) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00138-9 · Cited by 29 · see citing works
“medical image analysis [2–5], languageprocessing [6, 7], and tumor or cancer cell detection [8–12].”
Background
Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression
Msosa, Y. J., Grauslys, A., Zhou, Y., Wang, T., Buchan, I. E., Langan, P., Foster, S., Walker, M., Pearson, M. G., Folarin, A., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3312011 · Cited by 29 · see citing works
“Such NLP tools and methods can support mental health research and care delivery in clinical practice [24].”
Method
Application of Machine Learning in FPGA EDA Tool Development
Goswami, P., & Bhatia, D. K. (2023) · IEEE Access · doi:10.1109/access.2023.3322358 · Cited by 29 · see citing works
“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.”
Background
Revolutionizing 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 · see citing works
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 30 · see citing works
“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).”
Method
Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review
Scherbakov, D., Hubig, N., Lenert, L. A., Alekseyenko, A. V., & Obeid, J. S. (2024) · JMIR Mental Health · doi:10.2196/67192 · Cited by 34 · see citing works
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 29 · see citing works
“While machine learning has shown promise in streamlining literature retrieval, its accuracy often falls short of desired standards [7-9].”
Generalized hesitant fuzzy numbers and their application in solving MADM problems based on TOPSIS method
Keikha, A. (2022) · Soft Computing · doi:10.1007/s00500-022-06995-z · Cited by 29 · see citing works
“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.”
Background
Mental 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 28 · see citing works
Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users
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 27 · see citing works
SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques
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 27 · see citing works
Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations
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 27 · see citing works
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 27 · see citing works
“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.”
Background
Artificial Intelligence for the development of qualitative studies
Gamboa, A. J. P., & Díaz‐Guerra, D. D. (2023) · LatIA · doi:10.62486/latia20234 · Cited by 27 · see citing works
Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features
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 · see citing works
Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review
Ibrahim, S., Li, M., Patel, J., & Katapally, T. (2025) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2025.109859 · Cited by 26 · see citing works
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 · see citing works
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 26 · see citing works
“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 · see citing works
Healthcare Systems and Artificial Intelligence: Focus on Challenges and the International Regulatory Framework
Romagnoli, A., Ferrara, F., Langella, R., & Zovi, A. (2024) · Pharmaceutical Research · doi:10.1007/s11095-024-03685-3 · Cited by 28 · see citing works
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 28 · see citing works
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 · see citing works
Natural language processing-driven state machines to extract social factors from unstructured clinical documentation
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 · see citing works
“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.”
Method
Language 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 24 · see citing works
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 25 · see citing works
“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.”
BackgroundMethod
Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning
Quillivic, 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 · see citing works
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 · see citing works
“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 · see citing works
Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service
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 23 · see citing works
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 · see citing works
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 22 · see citing works
“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].”
Background
Emotion‐aware psychological first aid: Integrating BERT‐based emotional distress detection with Psychological First Aid‐Generative Pre‐Trained Transformer chatbot for mental health support
Taiwo, O., & Al‐Bander, B. (2025) · Cognitive Computation and Systems · doi:10.1049/ccs2.12116 · Cited by 26 · see citing works
Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview
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 · see citing works
“Natural language processing (NLP) and machine learning (ML) have expanded how mental health conditions can be identified (5, 6).”
Background
Depression 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 22 · see citing works
“Not surprisingly, the common problem is that researchers use datasets that are small or too specific [7].”
TISE-LSTM: A LSTM model for precipitation nowcasting with temporal interactions and spatial extract blocks
Zheng, C., Tao, Y., Zhang, J., Xun, L., Li, T., & Yan, Q. Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127700 · Cited by 21 · see citing works
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 · see citing works

Artificial Intelligence in Psychiatry: A New Paradigm
Gerantia, M. (2024) · European Psychiatry · doi:10.1192/j.eurpsy.2024.442 · Cited by 2 · see citing works
Natural language processing for mental health interventions: a systematic review and research framework
Malgaroli, M., Hull, T. D., Zech, J. M., & Althoff, T. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02592-2 · Cited by 217 · see citing works
Narrative Analysis of Natural Language Processing Applied to the Mental Health Monitoring System
Hemamalini, U., & Shina, M. K. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1408000007
An in-depth exploration of machine learning methods for mental health state detection: a systematic review and analysis
Hasan, M. J., Shifat, S. H., Matubber, J., Hossain, R., Rahman, M. A., Haque, B. M. T., & Hossen, M. J. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1724348 · Cited by 8 · see citing works
Artificial Intelligence in Psychiatry: A Comprehensive Literature Review
Gerantia, M. (2024) · European Psychiatry · doi:10.1192/j.eurpsy.2024.173 · Cited by 2 · see citing works

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: Le Glaz, Aziliz
Haralambous, Yannis
Kim-Dufor, Deok-Hee
Lenca, Philippe
Billot, Romain
Ryan, Taylor C
Marsh, Jonathan
DeVylder, Jordan
Walter, Michel
Berrouiguet, Sofian
Lemey, Christophe
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)
DOI: 10.2196/15708
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 ›

Are you an author, or do you see a problem with this page? Request a review

Machine learning and natural language processing in mental health: systematic review

Abstract

Integrity signals

Claims the paper makes

Self-check

Cites

Cited by

Related

Record

About these checks