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Ibrahim ST, Li M, Patel J, Katapally TR. Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review. Comput Biol Med 2025; 188:109859. [PMID: 39986200 DOI: 10.1016/j.compbiomed.2025.109859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 02/07/2025] [Accepted: 02/11/2025] [Indexed: 02/24/2025]
Abstract
BACKGROUND The global mental health crisis has created barriers to youth mental healthcare, leaving many disorders unaddressed. Precision prevention, which identifies individual risks, offers the potential for tailored interventions. While natural language processing (NLP) has shown promise in the early detection of mental health disorders, no review has examined its role in youth mental health detection. We hypothesize that NLP can improve early detection and personalized care in mental healthcare among youth. METHODOLOGY After screening 1197 articles from 5 databases, 12 papers were included covering six categories: (1) mental health disorders, (2) data sources, (3) NLP objective for mental health detection, (4) annotation and validation techniques, (5) linguistic markers, and (6) performance and evaluation. Study quality was assessed using Hawker's checklist for disparate study designs. RESULTS Most studies focused on suicide risk (42 %), depression (25 %), and stress (17 %). Social media (42 %) and interviews (33 %) were the most common data sources, with linguistic inquiry and word count and support vector machines frequently used for analysis. While most studies were exploratory, one implemented a real-time tool for detecting mental health risks. Validation methods, including precision and recall metrics, showed strong predictive performance. CONCLUSIONS This review highlights the potential of NLP in youth mental health detection, addressing challenges such as bias, data quality, and ethical concerns. Future research should refine NLP models using diverse, multimodal datasets, addressing data imbalance, and improving real-time detection. Exploring transformer-based models and ensuring ethical, inclusive data handling will be key to advancing NLP-driven interventions.
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Affiliation(s)
- Sheriff Tolulope Ibrahim
- DEPtH Lab, School of Health Studies, Faculty of Health Sciences, Western University, London, Ontario, N6A 5B9, Canada; Children's Health Research Institute, Lawson Health Research Institute, 750 Base Line Road East, Suite 300, London, Ontario, N6A 5B9, Canada
| | - Madeline Li
- DEPtH Lab, School of Health Studies, Faculty of Health Sciences, Western University, London, Ontario, N6A 5B9, Canada
| | - Jamin Patel
- DEPtH Lab, School of Health Studies, Faculty of Health Sciences, Western University, London, Ontario, N6A 5B9, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, N6A 5B9, Canada
| | - Tarun Reddy Katapally
- DEPtH Lab, School of Health Studies, Faculty of Health Sciences, Western University, London, Ontario, N6A 5B9, Canada; Children's Health Research Institute, Lawson Health Research Institute, 750 Base Line Road East, Suite 300, London, Ontario, N6A 5B9, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, N6A 5B9, Canada.
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Hartnagel L, Ebner‐Priemer UW, Foo JC, Streit F, Witt SH, Frank J, Limberger MF, Horn AB, Gilles M, Rietschel M, Sirignano L. Linguistic style as a digital marker for depression severity: An ambulatory assessment pilot study in patients with depressive disorder undergoing sleep deprivation therapy. Acta Psychiatr Scand 2025; 151:348-357. [PMID: 38987940 PMCID: PMC11787911 DOI: 10.1111/acps.13726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 05/28/2024] [Accepted: 06/22/2024] [Indexed: 07/12/2024]
Abstract
BACKGROUND Digital phenotyping and monitoring tools are the most promising approaches to automatically detect upcoming depressive episodes. Especially, linguistic style has been seen as a potential behavioral marker of depression, as cross-sectional studies showed, for example, less frequent use of positive emotion words, intensified use of negative emotion words, and more self-references in patients with depression compared to healthy controls. However, longitudinal studies are sparse and therefore it remains unclear whether within-person fluctuations in depression severity are associated with individuals' linguistic style. METHODS To capture affective states and concomitant speech samples longitudinally, we used an ambulatory assessment approach sampling multiple times a day via smartphones in patients diagnosed with depressive disorder undergoing sleep deprivation therapy. This intervention promises a rapid change of affective symptoms within a short period of time, assuring sufficient variability in depressive symptoms. We extracted word categories from the transcribed speech samples using the Linguistic Inquiry and Word Count. RESULTS Our analyses revealed that more pleasant affective momentary states (lower reported depression severity, lower negative affective state, higher positive affective state, (positive) valence, energetic arousal and calmness) are mirrored in the use of less negative emotion words and more positive emotion words. CONCLUSION We conclude that a patient's linguistic style, especially the use of positive and negative emotion words, is associated with self-reported affective states and thus is a promising feature for speech-based automated monitoring and prediction of upcoming episodes, ultimately leading to better patient care.
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Affiliation(s)
- Lisa‐Marie Hartnagel
- Mental mHealth Lab, Institute of Sports and Sports ScienceKarlsruhe Institute of TechnologyKarlsruheGermany
| | - Ulrich W. Ebner‐Priemer
- Mental mHealth Lab, Institute of Sports and Sports ScienceKarlsruhe Institute of TechnologyKarlsruheGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Jerome C. Foo
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
- Institute for Psychopharmacology, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
- Neuroscience and Mental Health InstituteUniversity of AlbertaEdmontonAlbertaCanada
- Department of Psychiatry, College of Health SciencesUniversity of AlbertaEdmontonAlbertaCanada
| | - Fabian Streit
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Matthias F. Limberger
- Mental mHealth Lab, Institute of Sports and Sports ScienceKarlsruhe Institute of TechnologyKarlsruheGermany
| | - Andrea B. Horn
- University Research Priority Program (URPP) Dynamics of Healthy Aging, Healthy Longevity CenterUniversity of ZürichZürichSwitzerland
| | - Maria Gilles
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Lea Sirignano
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
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Cruz-Gonzalez P, He AWJ, Lam EP, Ng IMC, Li MW, Hou R, Chan JNM, Sahni Y, Vinas Guasch N, Miller T, Lau BWM, Sánchez Vidaña DI. Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications. Psychol Med 2025; 55:e18. [PMID: 39911020 PMCID: PMC12017374 DOI: 10.1017/s0033291724003295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 10/26/2024] [Accepted: 11/26/2024] [Indexed: 02/07/2025]
Abstract
Artificial intelligence (AI) has been recently applied to different mental health illnesses and healthcare domains. This systematic review presents the application of AI in mental health in the domains of diagnosis, monitoring, and intervention. A database search (CCTR, CINAHL, PsycINFO, PubMed, and Scopus) was conducted from inception to February 2024, and a total of 85 relevant studies were included according to preestablished inclusion criteria. The AI methods most frequently used were support vector machine and random forest for diagnosis, machine learning for monitoring, and AI chatbot for intervention. AI tools appeared to be accurate in detecting, classifying, and predicting the risk of mental health conditions as well as predicting treatment response and monitoring the ongoing prognosis of mental health disorders. Future directions should focus on developing more diverse and robust datasets and on enhancing the transparency and interpretability of AI models to improve clinical practice.
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Affiliation(s)
- Pablo Cruz-Gonzalez
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | - Aaron Wan-Jia He
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Elly PoPo Lam
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Ingrid Man Ching Ng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Mandy Wingman Li
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Rangchun Hou
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jackie Ngai-Man Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Yuvraj Sahni
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Nestor Vinas Guasch
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Tiev Miller
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Benson Wui-Man Lau
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
- Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Dalinda Isabel Sánchez Vidaña
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
- Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
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Dominiak M, Gędek A, Antosik AZ, Mierzejewski P. Mobile health for mental health support: a survey of attitudes and concerns among mental health professionals in Poland over the period 2020-2023. Front Psychiatry 2024; 15:1303878. [PMID: 38559395 PMCID: PMC10978719 DOI: 10.3389/fpsyt.2024.1303878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Mobile health (mHealth) has emerged as a dynamic sector supported by technological advances and the COVID-19 pandemic and have become increasingly applied in the field of mental health. Aim The aim of this study was to assess the attitudes, expectations, and concerns of mental health professionals, including psychiatrists, psychologists, and psychotherapists, towards mHealth, in particular mobile health self-management tools and telepsychiatry in Poland. Material and methods This was a survey conducted between 2020 and 2023. A questionnaire was administered to 148 mental health professionals, covering aspects such as telepsychiatry, mobile mental health tools, and digital devices. Results The majority of professionals expressed readiness to use telepsychiatry, with a peak in interest during the COVID-19 pandemic, followed by a gradual decline from 2022. Concerns about telepsychiatry were reported by a quarter of respondents, mainly related to difficulties in correctly assessing the patient's condition, and technical issues. Mobile health tools were positively viewed by professionals, with 86% believing they could support patients in managing mental health and 74% declaring they would recommend patients to use them. Nevertheless, 29% expressed concerns about the effectiveness and data security of such tools. Notably, the study highlighted a growing readiness among mental health professionals to use new digital technologies, reaching 84% in 2023. Conclusion These findings emphasize the importance of addressing concerns and designing evidence-based mHealth solutions to ensure long-term acceptance and effectiveness in mental healthcare. Additionally, the study highlights the need for ongoing regulatory efforts to safeguard patient data and privacy in the evolving digital health landscape.
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Affiliation(s)
- Monika Dominiak
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Adam Gędek
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
- Praski Hospital, Warsaw, Poland
| | - Anna Z. Antosik
- Department of Psychiatry, Faculty of Medicine, Collegium Medicum, Cardinal Wyszynski University, Warsaw, Poland
| | - Paweł Mierzejewski
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
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Seyedi S, Griner E, Corbin L, Jiang Z, Roberts K, Iacobelli L, Milloy A, Boazak M, Bahrami Rad A, Abbasi A, Cotes RO, Clifford GD. Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study. JMIR Ment Health 2023; 10:e48517. [PMID: 37906217 PMCID: PMC10646674 DOI: 10.2196/48517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/25/2023] [Accepted: 09/12/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.
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Affiliation(s)
- Salman Seyedi
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Emily Griner
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Lisette Corbin
- Department of Psychiatry, Duke University Health, Durham, NC, United States
| | - Zifan Jiang
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Kailey Roberts
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Luca Iacobelli
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Aaron Milloy
- Infection Prevention Department, Emory Healthcare, Atlanta, GA, United States
| | - Mina Boazak
- Animo Sano Psychiatry, Durham, NC, United States
| | - Ali Bahrami Rad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Ahmed Abbasi
- Department of Information Technology, Analytics, and Operations, University of Notre Dame, Notre Dame, IN, United States
| | - Robert O Cotes
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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