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Hawes MT, Schwartz HA, Son Y, Klein DN. Predicting adolescent depression and anxiety from multi-wave longitudinal data using machine learning. Psychol Med 2023; 53:6205-6211. [PMID: 36377499 DOI: 10.1017/s0033291722003452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND This study leveraged machine learning to evaluate the contribution of information from multiple developmental stages to prospective prediction of depression and anxiety in mid-adolescence. METHODS A community sample (N = 374; 53.5% male) of children and their families completed tri-annual assessments across ages 3-15. The feature set included several important risk factors spanning psychopathology, temperament/personality, family environment, life stress, interpersonal relationships, neurocognitive, hormonal, and neural functioning, and parental psychopathology and personality. We used canonical correlation analysis (CCA) to reduce the large feature set to a lower dimensional space while preserving the longitudinal structure of the data. Ablation analysis was conducted to evaluate the relative contributions to prediction of information gathered at different developmental periods and relative to previous disorder status (i.e. age 12 depression or anxiety) and demographics (sex, race, ethnicity). RESULTS CCA components from individual waves predicted age 15 disorder status better than chance across ages 3, 6, 9, and 12 for anxiety and 9 and 12 for depression. Only the components from age 12 for depression, and ages 9 and 12 for anxiety, improved prediction over prior disorder status and demographics. CONCLUSIONS These findings suggest that screening for risk of adolescent depression can be successful as early as age 9, while screening for risk of adolescent anxiety can be successful as early as age 3. Assessing additional risk factors at age 12 for depression, and going back to age 9 for anxiety, can improve screening for risk at age 15 beyond knowing standard demographics and disorder history.
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Affiliation(s)
- Mariah T Hawes
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - H Andrew Schwartz
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Youngseo Son
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Daniel N Klein
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
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Chokka P, Bender A, Brennan S, Ahmed G, Corbière M, Dozois DJA, Habert J, Harrison J, Katzman MA, McIntyre RS, Liu YS, Nieuwenhuijsen K, Dewa CS. Practical pathway for the management of depression in the workplace: a Canadian perspective. Front Psychiatry 2023; 14:1207653. [PMID: 37732077 PMCID: PMC10508062 DOI: 10.3389/fpsyt.2023.1207653] [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: 04/17/2023] [Accepted: 08/09/2023] [Indexed: 09/22/2023] Open
Abstract
Major depressive disorder (MDD) and other mental health issues pose a substantial burden on the workforce. Approximately half a million Canadians will not be at work in any week because of a mental health disorder, and more than twice that number will work at a reduced level of productivity (presenteeism). Although it is important to determine whether work plays a role in a mental health condition, at initial presentation, patients should be diagnosed and treated per appropriate clinical guidelines. However, it is also important for patient care to determine the various causes or triggers including work-related factors. Clearly identifying the stressors associated with the mental health disorder can help clinicians to assess functional limitations, develop an appropriate care plan, and interact more effectively with worker's compensation and disability programs, as well as employers. There is currently no widely accepted tool to definitively identify MDD as work-related, but the presence of certain patient and work characteristics may help. This paper seeks to review the evidence specific to depression in the workplace, and provide practical tips to help clinicians to identify and treat work-related MDD, as well as navigate disability issues.
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Affiliation(s)
- Pratap Chokka
- Department of Psychiatry, University of Alberta, Grey Nuns Hospital, Edmonton, AB, Canada
| | - Ash Bender
- Work, Stress and Health Program, The Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Stefan Brennan
- Department of Psychiatry, University of Saskatchewan, Royal University Hospital, Saskatoon, SK, Canada
| | - Ghalib Ahmed
- Department of Family Medicine and Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Marc Corbière
- Department of Education, Career Counselling, Université du Québec à Montréal, Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
| | - David J. A. Dozois
- Department of Psychology, University of Western Ontario, London, ON, Canada
| | - Jeff Habert
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - John Harrison
- Metis Cognition Ltd., Kilmington, United Kingdom; Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, United Kingdom; Alzheimercentrum, AUmc, Amsterdam, Netherlands
| | - Martin A. Katzman
- START Clinic for the Mood and Anxiety Disorders, Toronto, ON, Canada; Department of Psychiatry, Northern Ontario School of Medicine, and Department of Psychology, Lakehead University, Thunder Bay, ON, Canada
| | - Roger S. McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Yang S. Liu
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Karen Nieuwenhuijsen
- Department of Public and Occupational Health, Coronel Institute of Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Carolyn S. Dewa
- Department of Psychiatry and Behavioural Sciences, University of California, Davis, Davis, CA, United States
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Liu YS, Hankey JR, Chokka S, Chokka PR, Cao B. Individualized identification of sexual dysfunction of psychiatric patients with machine-learning. Sci Rep 2022; 12:9599. [PMID: 35688888 PMCID: PMC9187754 DOI: 10.1038/s41598-022-13642-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/12/2022] [Indexed: 11/30/2022] Open
Abstract
Sexual dysfunction (SD) is prevalent in patients with mental health disorders and can significantly impair their quality of life. Early recognition of SD in a clinical setting may help patients and clinicians to optimize treatment options of SD and/or other primary diagnoses taking SD risk into account and may facilitate treatment compliance. SD identification is often overlooked in clinical practice; we seek to explore whether patients with a high risk of SD can be identified at the individual level by assessing known risk factors via a machine learning (ML) model. We assessed 135 subjects referred to a tertiary mental health clinic in a Western Canadian city using health records data, including age, sex, physician’s diagnoses, drug treatment, and the Arizona Sexual Experiences Scale (ASEX). A ML model was fitted to the data, with SD status derived from the ASEX as target outcomes and all other variables as predicting variables. Our ML model was able to identify individual SD cases—achieving a balanced accuracy of 0.736, with a sensitivity of 0.750 and a specificity of 0.721—and identified major depressive disorder and female sex as risk factors, and attention deficit hyperactivity disorder as a potential protective factor. This study highlights the utility of SD screening in a psychiatric clinical setting, demonstrating a proof-of-concept ML approach for SD screening in psychiatric patients, which has marked potential to improve their quality of life.
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Affiliation(s)
- Yang S Liu
- Chokka Center for Integrative Health, 301 - 2603 Hewes Way NW, Edmonton, AB, T6L 6W6, Canada.,Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 2B7, Canada
| | - Jeffrey R Hankey
- Chokka Center for Integrative Health, 301 - 2603 Hewes Way NW, Edmonton, AB, T6L 6W6, Canada.,Department of Psychology, York University, Toronto, Canada
| | - Stefani Chokka
- Chokka Center for Integrative Health, 301 - 2603 Hewes Way NW, Edmonton, AB, T6L 6W6, Canada
| | - Pratap R Chokka
- Chokka Center for Integrative Health, 301 - 2603 Hewes Way NW, Edmonton, AB, T6L 6W6, Canada. .,Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 2B7, Canada.
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 2B7, Canada.
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Lin E, Kuo PH, Lin WY, Liu YL, Yang AC, Tsai SJ. Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach. J Pers Med 2021; 11:597. [PMID: 34202750 PMCID: PMC8308113 DOI: 10.3390/jpm11070597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/14/2021] [Accepted: 06/22/2021] [Indexed: 12/16/2022] Open
Abstract
In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Po-Hsiu Kuo
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan; (P.-H.K.); (W.-Y.L.)
| | - Wan-Yu Lin
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan; (P.-H.K.); (W.-Y.L.)
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County 35053, Taiwan;
| | - Albert C. Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USA;
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- Division of Psychiatry, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
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