Academic literature on the topic 'Mental-health classification'

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Journal articles on the topic "Mental-health classification"

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Daksh, Gupta, Markale Aashay, and Kulkarni Rishabh. "Mental Health Quantifier." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 187–90. https://doi.org/10.35940/ijeat.E2694.0610521.

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The definition of mental disorders describes them as “health conditions involving changes in emotion, thinking or behavior or a combination of these”. Contemporary societies of 2020 still fall short in recognizing some of the most common afflictions as actual problems in people. Some of those are depression, anxiety and stress disorders. This paper proposes a Machine Learning based approach wherein the analysis of the multiple-choice inputs along with a neatly curated questionnaire based on feature extraction will be done and then supervised classification algorithms will be used t
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Jacob, K. S., and Vikram Patel. "Classification of mental disorders: a global mental health perspective." Lancet 383, no. 9926 (2014): 1433–35. http://dx.doi.org/10.1016/s0140-6736(13)62382-x.

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Sutranggono, Abi Nizar, Riyanarto Sarno, and Imam Ghozali. "Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media." Journal of Information and Communication Technology 23, no. 1 (2024): 77–104. http://dx.doi.org/10.32890/jict2024.23.1.4.

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Mental health disorders pose a significant global public health challenge. Social media data provides insights into these conditions.Analysing text can help identify indications of mental health disorders through text-based analysis. However, despite the largenumber of studies on the analysis of mental health disorders, the predominant algorithm in the existing literature is the Multi-ClassSingle-Level (MCSL) classification algorithm, which is often used for simple classification tasks involving a limited number of classes.Typically, these classes are binary, representing either an unhealthy o
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Gupta, Daksh, Aashay Markale, and Rishabh Kulkarni. "Mental Health Quantifier." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 187–90. http://dx.doi.org/10.35940/ijeat.e2694.0610521.

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The definition of mental disorders describes them as “health conditions involving changes in emotion, thinking or behavior or a combination of these”. Contemporary societies of 2020 still fall short in recognizing some of the most common afflictions as actual problems in people. Some of those are depression, anxiety and stress disorders. This paper proposes a Machine Learning based approach wherein the analysis of the multiple-choice inputs along with a neatly curated questionnaire based on feature extraction will be done and then supervised classification algorithms will be used to generate a
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Yihai, Wei. "DEVELOPMENT OF A CLASSIFICATION MODEL FOR MENTAL HEALTH." Young Scholars Journal, no. 5-6 (2022): 12–18. http://dx.doi.org/10.29013/ysj-22-5.6-12-18.

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Ahamat, Boolang R., and Helen Minnis. "Infant mental health: classification and relevance for clinicians." Psychiatrist 36, no. 6 (2012): 230–34. http://dx.doi.org/10.1192/pb.bp.110.033969.

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SummaryInfant mental health is a growing research area, but findings have not generally translated into new service developments. Recognising mental health problems in young infants is relevant for clinicians in all mental health specialties, but it can be a particular challenge to make diagnoses in very young children. Mental health classification systems are fraught with the difficulties of standardising diagnoses for infants, while trying to provide a clinically useful and relevant framework. The diagnostic classification DC:0–3 appears to have strengths, for example, a clear space to consi
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Grassi, Gaddomaria. "Mental Health Department accreditation. Classification and assessment criteria." Epidemiologia e Psichiatria Sociale 13, no. 2 (2004): 126–33. http://dx.doi.org/10.1017/s1121189x00003353.

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SummaryAims – To compare different accreditation categories in Italy and to suggest some criteria to assess them. Methods – The A. compare specific characteristics of different model of normative and professional accreditation and suggest some criteria to distinguish different type of accreditation programme and to assess quality and usefulness of an accreditation programme. Results and Conclusions – Different types of accreditation are attributed to some basic criteria: voluntary/by law, high/low stan- dard, assessment object. Criteria to assess the usefulness are: check list accuracy, suppor
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Wetzel, Janice Wood. "Universal Mental Health Classification Systems: Reclaiming Women's Experience." Affilia 6, no. 3 (1991): 8–31. http://dx.doi.org/10.1177/088610999100600302.

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Basuki, Setio, Rizky Indrabayu, and Nico Ardia Effendy. "Automatic Categorization of Mental Health Frame in Indonesian X (Twitter) Text using Classification and Topic Detection Techniques." Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika 10, no. 2 (2025): 104–10. https://doi.org/10.23917/khif.v10i2.3328.

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This paper aims to develop a machine learning model to detect mental health frames in Indonesian-language tweets on the X (Twitter) platform. This research is motivated by the gap in automatically detecting mental health frames, despite the importance of mental health issues in Indonesia. This paper addresses the problem by applying classification and topic detection methods across various mental health frames through multiple stages. First, this paper examines various mental health frames, resulting in 7 main labels: Awareness, Classification, Feelings and Problematization, Accessibility and
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Keeley, Jared W. "Accounting for Social Processes in the Development of Diagnostic Classification Systems: Commentary on the “Standards and Guidelines for the Development of Diagnostic Nomenclatures and Alternatives in Mental Health Research and Practice”." Journal of Humanistic Psychology 59, no. 3 (2018): 446–52. http://dx.doi.org/10.1177/0022167818788494.

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Diagnostic classifications of mental disorders are complex systems that integrate both scientific and social priorities. The Task Force on Developing Humanistic Approaches to Psychological Diagnosis of the Society for Humanistic Psychology has released a set of guidelines intended to guide the development of mental health classifications. This commentary argues that the attention paid in the guidelines to social processes is a desirable and inherent component of the development of any classification. Social influences regarding the definition of the concepts and the processes by which people d
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Dissertations / Theses on the topic "Mental-health classification"

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Wittchen, Hans-Ulrich, Samia Härtling, and Jürgen Hoyer. "Psychotherapy and Mental Health as a Psychological Science Discipline." Karger, 2015. https://tud.qucosa.de/id/qucosa%3A71679.

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The psychological sciences offer a large spectrum of theories, principles, and methodological approaches to understand mental health, normal and abnormal functions and behaviours, as well as mental disorders. Based on continued research progress, psychology has derived a wide range of effective interventions for behaviour change and the prevention, treatment and rehabilitation of mental disorders. Thus, psychology and clinical psychology in particular should be regarded as the ‘mother’ science for psychotherapy and psychotherapeutic practice. This paper provides a selective overview of the sco
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Holz, Manuel. "Health Inequalities in Germany: Assessing Differences in Health of Migrants and Native Germans Using a Propensity Score Matching Approach and the SF-12 Physical and Mental Health Scale." Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A38385.

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The aim of the study is to compare health outcomes of migrants and the native German population, testing for the existence of a Healthy Immigrant Effect (HIE). The study contributes a broad theoretical analysis of the HIE and makes use of a wide spectrum of variables to model health (using the SF-12). The HIE is marked by an observed health advantage for migrants, when compared to the host population, which declines with the years since migration. Assessing different types of selection processes, it is assumed that mass migration to post-industrial countries is characterized by favouring the
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Kalckreuth, Sophie, Friederike Trefflich, and Christine Rummel-Kluge. "Mental health related Internet use among psychiatric patients: a cross-sectional analysis." BioMed Central, 2014. https://ul.qucosa.de/id/qucosa%3A13091.

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Background: The Internet is of great importance in today’s health sector, as most Internet users utilize online functions for health related purposes. Concerning the mental health care sector, little data exist about the Internet use of psychiatric patients. It is the scope of this current study to analyze the quantity and pattern of Internet usage among mental health patients. Methods: Patients from all services of the Department of Psychiatry at a university hospital were surveyed by completing a 29-item questionnaire. The data analysis included evaluation of frequencies, as well as group co
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Volkert, Jana, Sylke Andreas, Martin Härter, et al. "Predisposing, enabling, and need factors of service utilization in the elderly with mental health problems." Cambridge University Press, 2018. https://tud.qucosa.de/id/qucosa%3A70712.

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Background: Empirical data on the use of services due to mental health problems in older adults in Europe is lacking. The objective of this study is to identify factors associated with service utilization in the elderly. Methods: As part of the MentDis_ICF65+ study, N = 3,142 people aged 65–84 living in the community in six European and associated countries were interviewed. Based on Andersen’s behavioral model predisposing, enabling, and need factors were analyzed with logistic regression analyses. Results: Overall, 7% of elderly and 11% of those with a mental disorder had used a service due
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Wille, Nora, Susanne Bettge, Hans-Ulrich Wittchen, and Ulrike Ravens-Sieberer. "How impaired are children and adolescents by mental health problems? Results of the BELLA study." Technische Universität Dresden, 2008. https://tud.qucosa.de/id/qucosa%3A26677.

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Background: The consideration of impairment plays a crucial role in detecting significant mental health problems in children whose symptoms do not meet diagnostic criteria. The assessment of impairment may be particularly relevant when only short screening instruments are applied in epidemiological surveys. Furthermore, differences between childrens’ and parents’ perceptions of present impairment and impairing symptoms are of interest with respect to treatment-seeking behaviour. Objectives: The objectives were to assess parent- and self-reported impairment due to mental health problems in a
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Herget, Sabine, Almut Rudolph, Anja Hilbert, and Susann Blüher. "Psychosocial status and mental health in adolescents before and after bariatric surgery." Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-149664.

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Objective: As long-term results of conservative treatment for obesity are discouraging, bariatric surgery is becoming a treatment option for extremely obese adolescents. However, mental and behavioral problems need to be respected when treating this vulnerable target group. Methods: A detailed systematic literature review on pre- and post-operative depressive, anxiety and eating disorder symptoms of adolescent patients was performed in PsychINFO, PubMed and Medline electronic databases. Results: Twelve studies met the inclusion criteria. Although strength of evidence was limited, results sugge
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Ravens-Sieberer, Ulrike, Nora Wille, Michael Erhart, et al. "Prevalence of mental health problems among children and adolescents in Germany: Results of the BELLA study within the National Health Interview and Examination Survey." Technische Universität Dresden, 2008. https://tud.qucosa.de/id/qucosa%3A26672.

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Background: Over the past decades the public health relevance of mental health conditions in children and adolescents has been of growing concern. However, so far no detailed epidemiological data has been available for a representative national sample in Germany. Objectives: The present paper reports prevalence rates of general and specific mental health problems among children and adolescents in Germany and describes the link between symptoms and impairment as well as the treatment situation. Methods: The mental health module (BELLA study) examines mental health problems in a representativ
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Jacobi, Frank, Hans-Ulrich Wittchen, C. Hölting, et al. "Prevalence, co-morbidity and correlates of mental disorders in the general population: results from the German Health Interview and Examination Survey (GHS)." Cambridge University Press, 2004. https://tud.qucosa.de/id/qucosa%3A26137.

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Background. The German National Health Interview and Examination Survey (GHS) is the first government mandated nationwide study to investigate jointly the prevalence of somatic and mental disorders within one study in the general adult population in Germany. This paper reports results from its Mental Health Supplement (GHS-MHS) on 4-week 12-month, and selected lifetime prevalence of a broad range of DSM-IV mental disorders, their co-morbidity and correlates in the community. Methods. The sample of the GHS-MHS (n=4181; multistage stratified random sample drawn from population registries ; cond
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Gopchandani, Sandhya. "Using Word Embeddings to Explore the Language of Depression on Twitter." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1072.

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How do people discuss mental health on social media? Can we train a computer program to recognize differences between discussions of depression and other topics? Can an algorithm predict that someone is depressed from their tweets alone? In this project, we collect tweets referencing “depression” and “depressed” over a seven year period, and train word embeddings to characterize linguistic structures within the corpus. We find that neural word embeddings capture the contextual differences between “depressed” and “healthy” language. We also looked at how context around words may have changed ov
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Wittchen, Hans-Ulrich. "Women-specific mental disorders in DSM-V: are we failing again?" Technische Universität, 2010. https://tud.qucosa.de/id/qucosa%3A25035.

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Despite a wealth of studies on differences regarding the biobehavioral and social–psychological bases of mental disorders in men and women and repeated calls for increased attention, women-specific issues have so far not been comprehensively addressed in past diagnostic classification systems of mental disorders. There is also increasing evidence that this situation will not change significantly in the upcoming revisions of ICD-11 and DSM-V. This paper explores reasons for this continued failure, highlighting three major barriers: the fragmentation of the field of women's mental health researc
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Books on the topic "Mental-health classification"

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1946-, Stark Jack A., United States. President's Committee on Mental Retardation., and National Strategies Conference on Mental Illness in the Mentally Retarded (1985 : Washington, D.C.), eds. Mental retardation and mental health: Classification, diagnosis, treatment, services. Springer-Verlag, 1988.

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1946-, Stark Jack A., UnitedStates. President's Committee on Mental Retardation., and National Strategy Conference on Mental Illness in the Mentally Retarded (1985 : Washington), eds. Mental retardation andmental health: Classification, diagnosis, treatment, services. Springer-Verlag, 1988.

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Maj, Mario. Psychiatric diagnosis and classification. John Wiley, 2002.

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Carthew, Richard. Mental health services: How a classification of people and services describes and helps mental health services : a briefing paper. NHS Information Authority, 2000.

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American Association on Mental Retardation., ed. Mental retardation: Definition, classification, and systems of supports : workbook. American Association on Mental Retardation, 2002.

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(Organization), Zero to Three, ed. Diagnostic classification, 0-3: Diagnostic classification of mental health and developmental disorders of infancy and early childhood. Zero To Three/National Center for Clinical Infant Programs, 1994.

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American Psychiatric Association. Diagnostic and statistical manual of mental disorders. American Psychiatric Association, 1987.

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National Institute of Mental Health (U.S.). Division of Biometry and Epidemiology. Survey and Reports Branch. Additions and resident patients at end of year: State and county mental hospitals by age and diagnosis, by state, United States, 1982 : Repared by Survey and Reports Branch, Division of Biometry and Epidemiology, National Institute of Mental Health. U.S. Department of Health and Human Services, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, 1985.

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author, Gonzalez Gabriella, Gillen Emily Meredith author, Garnett Jeffrey author, et al., eds. The development and application of the RAND program classification tool. Rand Corporation, 2014.

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Saxena, Shekhar. Public health aspects of diagnosis and classification of mental and behavioral disorders: Refining the research agenda for DSM-5 and ICD-11. American Psychiatric Association, 2012.

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Book chapters on the topic "Mental-health classification"

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Klinkman, Michael, Christopher Dowrick, and Sandra Fortes. "Mental health classification in primary care." In Companion to Primary Care Mental Health. CRC Press, 2022. http://dx.doi.org/10.1201/9781846198465-13.

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Jones, Kevin Matthew. "Psychological Classification and Asylum Medical Statistics." In Mental Health in Historical Perspective. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-46154-5_6.

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O’Reilly, Michelle, and Jessica Nina Lester. "Classification Systems and Diagnostic Processes." In Examining Mental Health through Social Constructionism. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60095-6_3.

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Rebello, Tahilia J., Danielle M. Polland, Adina C. Heckelman, Elizabeth Magill, Geoffrey M. Reed, and Kathleen M. Pike. "Classification: The Foundation and Essential Catalyst for Advancing Mental Health Globally." In Innovations in Global Mental Health. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-70134-9_111-1.

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Rebello, Tahilia J., Danielle M. Polland, Adina C. Heckelman, Elizabeth Magill, Geoffrey M. Reed, and Kathleen M. Pike. "Classification: The Foundation and Essential Catalyst for Advancing Mental Health Globally." In Innovations in Global Mental Health. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57296-9_111.

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León, Carlos A. "Anxiety Disorders: Considerations from the Perspectives of Primary Mental Health Care." In Clinical Psychopathology Nomenclature and Classification. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4899-5049-9_132.

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Naseeba, B., G. Ravindranath, P. Pranay, K. Reddy Madhavi, and Sivaram Rajeyyagari. "Mental Health Chatbot Behaviour Classification and Heuristic Analysis." In Advanced Technologies and Societal Change. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-99-2832-3_19.

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Stiffler, Meghan C., and Bridget V. Dever. "History of Screening Practices, Mental Health Assessment, and Classification in the USA." In Mental Health Screening at School. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19171-3_2.

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L’Abate, Luciano. "Interactive Practice Exercises for Psychiatric Classification." In Sourcebook of Interactive Practice Exercises in Mental Health. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1354-8_11.

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Sartorius, Norman. "The Programmes of the World Health Organization Directed to the Improvement of Nomenclature and Classification in Mental Health." In Clinical Psychopathology Nomenclature and Classification. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4899-5049-9_3.

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Conference papers on the topic "Mental-health classification"

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Bajetha, Iesh, Bharti, Himanshu Singh, Ankur Pandey, Shivansh Singh, and Sukhpreet Singh. "CNNs for Anxiety Risk Classification and Prediction in Mental Health." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059033.

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Kaur, Guneet, Amandeep Kaur, and Gurvinder Singh. "BERT based Deep Learning Approach for Classification of Mental Health Disorders." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011294.

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Sharma, Neha, and Tharun Kumar Reddy Bollu. "Ablation studies towards interpretable ensemble deep neural networks for mental health classification." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724733.

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Tang, Jinwen, Qiming Guo, Yunxin Zhao, and Yi Shang. "Decoding Linguistic Nuances in Mental Health Text Classification Using Expressive Narrative Stories." In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2024. https://doi.org/10.1109/cogmi62246.2024.00035.

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Ibrahim, Izzah Maisarah Binti, Ruhaila Maskat, Azmi Bin Aminordin, and Noor Hasimah Ibrahim Teo. "Classification of Mental Health Conditions in Reddit Post using Multinomial Naïve Bayes Algorithm." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872671.

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Agarkar, Aarti Amod, Kalyani Ghuge, Shriram Dindore, Suvidha Dhakne, Atharva Dhakate, and Arya Mane. "Classification Models of Music Recommendation System for Mental Health Improvement: A Comparative Study." In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET). IEEE, 2025. https://doi.org/10.1109/icaet63349.2025.10932254.

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Duangchaemkarn, Khanita, Patiphan Khammarew, and Supavadee Aramvith. "Machine Learning-Based Classification of Mental Health State Using the DASS-21 Profile." In 2024 16th Biomedical Engineering International Conference (BMEiCON). IEEE, 2024. https://doi.org/10.1109/bmeicon64021.2024.10896316.

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Sharanya, Sharanya, and Praveena Kumari M K. "Impact of Music on Brain and Mental Health: Classification using Machine Learning Algorithms." In 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025. https://doi.org/10.1109/icaiss61471.2025.11042102.

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Kim, Laerdon. "A baseline for self-state identification and classification in mental health data: CLPsych 2025 Task." In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.clpsych-1.17.

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Yang, Ling. "Classification of Student’s Mental Health using Bidirectional Long Short-Term Memory with Self Attention Mechanism." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035111.

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Reports on the topic "Mental-health classification"

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Ahluwalia, Sangeeta C., Julia I. Bandini, Margaret Maglione, et al. Interventions To Improve Care of Bereaved Persons. Agency for Healthcare Research and Quality (AHRQ), 2025. https://doi.org/10.23970/ahrqepcsrbereaved.

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Objectives. Recently bereaved individuals face higher risks of adverse health outcomes, including increased risk of morbidity and mortality, increased risk of suicide, and lower functional status and quality of life. This systematic review assessed the available evidence on screening, diagnosing, and treating children and adults at risk for or diagnosed with grief disorders related to bereavement. Findings from this review informed a 2-day independent subject matter advisory panel convened on June 10, 2024, by the Substance Abuse and Mental Health Services Administration (SAMHSA) that assessed
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Gartlehner, Gerald, Sheila V. Patel, Laura Hart, et al. Implementing Recommended Mental Health and Substance Use Screening and Counseling Interventions in Primary Care Settings for Children and Adolescents. Agency for Healthcare Research and Quality, 2025. https://doi.org/10.23970/ahrqepcsrmentalhealth.

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Objectives. To assess the impact of implementation strategies for mental health and substance use screening and counseling for children and adolescents in primary care as recommended by the United States Preventive Services Task Force and Bright Futures Periodicity Schedule. Data sources. PubMed®, PsycInfo®, Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature®, as well as gray literature sources, reference lists, and technical experts. Review methods. We followed the Agency for Healthcare Research and Quality Methods Guide for Effectiveness and Comparative Effect
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Smyth, Emer, and Helen Russell. Trends in Disability Prevalence among Young People: Insights from the Growing Up in Ireland Study. ESRI, 2024. http://dx.doi.org/10.26504/rs192.

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Background to the study This report draws on analyses of the two cohorts of the Growing Up in Ireland (GUI) study to examine trends in the prevalence of disability among 13-year-olds over the decade 2011/2012 to 2021/2022. The report looks at changes over time in the size and composition of those with a long-lasting condition (LLC) or disability, at the presence of socio-emotional difficulties and/or depressive symptoms among these groups, and at a range of adolescent outcomes. The prevalence of long-lasting conditions and disability The prevalence of disability is highly dependent on the defi
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