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Karcher NR, Sotiras A, Niendam TA, Walker EF, Jackson JJ, Barch DM. Examining the Most Important Risk Factors Predicting Persistent and Distressing Psychotic-like Experiences in Youth. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00142-3. [PMID: 38849031 DOI: 10.1016/j.bpsc.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that differentiate clinically relevant PLEs early in development is important for improving our understanding of the etiopathogenesis of these experiences. Machine learning analyses examined the most important baseline factors distinguishing persistent distressing PLEs. METHODS Using Adolescent Brain Cognitive Development Study PLEs data over three time points (ages 9-13), individuals with persistent distressing PLEs (n=303), transient distressing PLEs (n=374), and demographically matched low-level PLEs groups were created. Random forest classification models were trained to distinguish among persistent distressing vs. low-level PLEs, transient distressing vs. low-level PLEs, and persistent distressing vs. transient distressing PLEs. Models were trained using identified baseline predictors as input features (i.e., cognitive, neural [cortical thickness, resting state functional connectivity (RSFC)], developmental milestone delays, internalizing symptoms, adverse childhood events). RESULTS The model distinguishing persistent distressing vs. low-level PLEs showed the highest accuracy (test sample accuracy=69.33%; 95% CI:61.29%-76.59%). The most important predictors included internalizing symptoms, adverse childhood events, and cognitive functioning. Models distinguishing persistent vs. transient distressing PLEs generally performed poorly. CONCLUSIONS Model performance metrics indicated that while most important factors overlapped across models (e.g., internalizing symptoms), adverse childhood events were especially important for predicting persistent distressing PLEs. Machine learning analyses proved useful for distinguishing the most clinically relevant group from the least clinically relevant group but showed limited ability to distinguish among clinically relevant groups that differed in PLE persistence.
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Affiliation(s)
- Nicole R Karcher
- Washington University School of Medicine, Department of Psychiatry.
| | - Aristeidis Sotiras
- Washington University School of Medicine, Department of Radiology; Washington University School of Medicine, Institute for Informatics, Data Science & Biostatistics
| | - Tara A Niendam
- University of California, Davis, Dept. of Psychiatry and Behavioral Sciences
| | | | - Joshua J Jackson
- Washington University in St. Louis, Department of Psychological and Brain Sciences
| | - Deanna M Barch
- Washington University School of Medicine, Department of Psychiatry; Washington University in St. Louis, Department of Psychological and Brain Sciences
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Bao J, Wan J, Li H, Sun F. Psychological pain and sociodemographic factors classified suicide attempt and non-suicidal self-injury in adolescents. Acta Psychol (Amst) 2024; 246:104271. [PMID: 38631150 DOI: 10.1016/j.actpsy.2024.104271] [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: 12/04/2023] [Revised: 04/01/2024] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
This study aimed to utilize machine learning to explore the psychological similarities and differences between suicide attempt (SA) and non-suicidal self-injury (NSSI), with a particular focus on the role of psychological pain. A total of 2385 middle school students were recruited using cluster sampling. The random forest algorithm was used with 25 predictors to develop classification models of SA and NSSI, respectively, and to estimate the importance scores of each predictor. Based on these scores and related theories, shared risk factors (control feature set) and distinct risk factors (distinction feature set) were selected and tested to distinguish between NSSI and SA. The machine learning algorithm exhibited fair to good performance in classifying SA history [Area Under Curves (AUCs): 0.65-0.87] and poor performance in classifying NSSI history (AUC: 0.61-0.68). The distinction feature set comprised pain avoidance, family togetherness, and deviant peer affiliation, while the control feature set included pain arousal, painful feelings, and crisis events. The distinction feature set slightly but stably outperformed the control feature set in classifying SA from NSSI. The three-dimensional psychological pain model, especially pain avoidance, might play a dominant role in understanding the similarities and differences between SA and NSSI.
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Affiliation(s)
- Jiamin Bao
- Department of Psychology, Renmin University of China, Beijing 100872, PR China
| | - Jiachen Wan
- Department of Psychology, Renmin University of China, Beijing 100872, PR China
| | - Huanhuan Li
- Department of Psychology, Renmin University of China, Beijing 100872, PR China.
| | - Fang Sun
- Department of Psychology, Renmin University of China, Beijing 100872, PR China
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Zhou SC, Zhou Z, Tang Q, Yu P, Zou H, Liu Q, Wang XQ, Jiang J, Zhou Y, Liu L, Yang BX, Luo D. Prediction of non-suicidal self-injury in adolescents at the family level using regression methods and machine learning. J Affect Disord 2024; 352:67-75. [PMID: 38360362 DOI: 10.1016/j.jad.2024.02.039] [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: 10/07/2022] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Adolescent non-suicidal self-injury (NSSI) is a major public health issue. Family factors are significantly associated with NSSI in adolescents, while studies on forecasting NSSI at the family level are still limited. In addition to regression methods, machine learning (ML) techniques have been recommended to improve the accuracy of family-level risk prediction for NSSI. METHODS Using a dataset of 7967 students and their primary caregivers from a cross-sectional study, logistic regression model and random forest model were used to test the forecasting accuracy of NSSI predictions at the family level. Cross-validation was used to assess model prediction performance, including the area under the receiver operator curve (AUC), precision, Brier score, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. RESULTS The top three important family-related predictors within the random forest algorithm included family function (importance:42.66), family conflict (importance:42.18), and parental depression (importance:27.21). The most significant family-related risk predictors and protective predictors identified by the logistic regression model were family history of mental illness (OR:2.25) and help-seeking behaviors of mental distress from parents (OR:0.65), respectively. The AUCs of the two models, logistic regression and random forest, were 0.852 and 0.835, respectively. LIMITATIONS The key limitation is that this cross-sectional survey only enabled the authors to examine predictors that were considered to be proximal rather than distal. CONCLUSIONS These findings highlight the significance of family-related factors in forecasting NSSI in adolescents. Combining both conventional statistical methods and ML methods to improve risk assessment of NSSI at the family level deserves attention.
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Affiliation(s)
- Si Chen Zhou
- Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China
| | - Zhaohe Zhou
- School of Basic Medical Sciences, Chengdu University, Chengdu, China
| | - Qi Tang
- Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China
| | - Ping Yu
- Wuhan Mental Health Center, Wuhan, China; Wuhan Hospital for Psychotherapy, Wuhan, China
| | - Huijing Zou
- Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China
| | - Qian Liu
- Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China
| | - Xiao Qin Wang
- Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China
| | - Jianmei Jiang
- The Central Hospital of Enshi Tujia Autonomous Prefecture, Enshi, China
| | - Yang Zhou
- Wuhan Mental Health Center, Wuhan, China; Wuhan Hospital for Psychotherapy, Wuhan, China
| | - Lianzhong Liu
- Wuhan Mental Health Center, Wuhan, China; Wuhan Hospital for Psychotherapy, Wuhan, China
| | - Bing Xiang Yang
- Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Dan Luo
- Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
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Gholi Zadeh Kharrat F, Gagne C, Lesage A, Gariépy G, Pelletier JF, Brousseau-Paradis C, Rochette L, Pelletier E, Lévesque P, Mohammed M, Wang J. Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec. PLoS One 2024; 19:e0301117. [PMID: 38568987 PMCID: PMC10990247 DOI: 10.1371/journal.pone.0301117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 04/05/2024] Open
Abstract
Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models' output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.
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Affiliation(s)
- Fatemeh Gholi Zadeh Kharrat
- Institut Intelligence et Données (IID), Université Laval, Québec, Québec, Canada
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Christian Gagne
- Institut Intelligence et Données (IID), Université Laval, Québec, Québec, Canada
| | - Alain Lesage
- Department of Psychiatry and Addiction, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Québec, Canada
| | - Geneviève Gariépy
- Centre for Surveillance and Applied Research, Health Promotion and Chronic Disease Prevention Branch, Public Health Agency of Canada, Ottawa, Canada
- Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, Canada
- Montreal Mental Health University Institute Research Center, Montreal, Canada
| | - Jean-François Pelletier
- Department of Psychiatry and Addiction, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Québec, Canada
| | - Camille Brousseau-Paradis
- Department of Psychiatry and Addiction, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Québec, Canada
| | - Louis Rochette
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Eric Pelletier
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Pascale Lévesque
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Mada Mohammed
- Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada
| | - JianLi Wang
- Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada
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Hasking PA, Robinson K, McEvoy P, Melvin G, Bruffaerts R, Boyes ME, Auerbach RP, Hendrie D, Nock MK, Preece DA, Rees C, Kessler RC. Development and evaluation of a predictive algorithm and telehealth intervention to reduce suicidal behavior among university students. Psychol Med 2024; 54:971-979. [PMID: 37732419 PMCID: PMC10939946 DOI: 10.1017/s0033291723002714] [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: 09/22/2023]
Abstract
BACKGROUND Suicidal behaviors are prevalent among college students; however, students remain reluctant to seek support. We developed a predictive algorithm to identify students at risk of suicidal behavior and used telehealth to reduce subsequent risk. METHODS Data come from several waves of a prospective cohort study (2016-2022) of college students (n = 5454). All first-year students were invited to participate as volunteers. (Response rates range: 16.00-19.93%). A stepped-care approach was implemented: (i) all students received a comprehensive list of services; (ii) those reporting past 12-month suicidal ideation were directed to a safety planning application; (iii) those identified as high risk of suicidal behavior by the algorithm or reporting 12-month suicide attempt were contacted via telephone within 24-h of survey completion. Intervention focused on support/safety-planning, and referral to services for this high-risk group. RESULTS 5454 students ranging in age from 17-36 (s.d. = 5.346) participated; 65% female. The algorithm identified 77% of students reporting subsequent suicidal behavior in the top 15% of predicted probabilities (Sensitivity = 26.26 [95% CI 17.93-36.07]; Specificity = 97.46 [95% CI 96.21-98.38], PPV = 53.06 [95% CI 40.16-65.56]; AUC range: 0.895 [95% CIs 0.872-0.917] to 0.966 [95% CIs 0.939-0.994]). High-risk students in the Intervention Cohort showed a 41.7% reduction in probability of suicidal behavior at 12-month follow-up compared to high-risk students in the Control Cohort. CONCLUSIONS Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students.
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Affiliation(s)
- Penelope A Hasking
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Faculty of Health Sciences, enAble Institute, Curtin University, Perth, Australia
| | - Kealagh Robinson
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Faculty of Health Sciences, enAble Institute, Curtin University, Perth, Australia
| | - Peter McEvoy
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Faculty of Health Sciences, enAble Institute, Curtin University, Perth, Australia
- Centre for Clinical Interventions, Perth, Australia
| | - Glenn Melvin
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Australia
| | | | - Mark E Boyes
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Faculty of Health Sciences, enAble Institute, Curtin University, Perth, Australia
| | - Randy P Auerbach
- Department of Psychiatry, Columbia University, New York, USA
- Division of Clinical Developmental Neuroscience, Sackler Institute, New York, USA
| | - Delia Hendrie
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Matthew K Nock
- Department of Psychology, Harvard University, Cambridge, USA
| | - David A Preece
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Faculty of Health Sciences, enAble Institute, Curtin University, Perth, Australia
| | - Clare Rees
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, USA
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Tang H, Miri Rekavandi A, Rooprai D, Dwivedi G, Sanfilippo FM, Boussaid F, Bennamoun M. Analysis and evaluation of explainable artificial intelligence on suicide risk assessment. Sci Rep 2024; 14:6163. [PMID: 38485985 PMCID: PMC10940617 DOI: 10.1038/s41598-024-53426-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 01/31/2024] [Indexed: 03/18/2024] Open
Abstract
This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.
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Affiliation(s)
- Hao Tang
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - Aref Miri Rekavandi
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - Dharjinder Rooprai
- Armadale Mental Health Service, Perth, Australia.
- Bethesda Clinic, Perth, Australia.
| | - Girish Dwivedi
- Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, Australia
- Department of Cardiology, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Frank M Sanfilippo
- School of Population and Global Health, University of Western Australia, Perth, Australia
| | - Farid Boussaid
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
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Somé NH, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Front Psychiatry 2024; 15:1291362. [PMID: 38501090 PMCID: PMC10944962 DOI: 10.3389/fpsyt.2024.1291362] [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: 09/09/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Background Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. Objective Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified. Methods A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed. Results The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study. Conclusion The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.
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Affiliation(s)
- Nibene H. Somé
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Pardis Noormohammadpour
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Shannon Lange
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Tennakoon G, Byrne EM, Vaithianathan R, Middeldorp CM. Using electronic health record data to predict future self-harm or suicidal ideation in young people treated by child and youth mental health services. Suicide Life Threat Behav 2023; 53:853-869. [PMID: 37578103 DOI: 10.1111/sltb.12988] [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: 02/15/2023] [Revised: 07/18/2023] [Accepted: 07/23/2023] [Indexed: 08/15/2023]
Abstract
INTRODUCTION Identifying young people who are at risk of self-harm or suicidal ideation (SHoSI) is a priority for mental health clinicians. We explore the utility of routinely collected data in developing a tool to aid early identification of those at risk. METHOD We used electronic health records of 4610 young people aged 5-19 years who were treated by Child and Youth Mental Health Services (CYMHS) in greater Brisbane, Australia. Two Lasso models were trained to predict the risk of future SHoSI in young people currently rated SHoSI; and those who were not. RESULTS For currently non-SHoSI children, an Area Under the Receiver Operating Characteristics (AUC) of 0.78 was achieved. Those with the highest risk were 4.97 (CI 4.35-5.66) times more likely to be categorized as SHoSI in the future. For current SHoSI children, the AUC was 0.62. CONCLUSION A prediction model with fair overall predictive power for currently non-SHoSI children was generated. Predicting persistence for SHoSI was more difficult. The electronic health records alone were not sufficient to discriminate at acceptable levels and may require adding unstructured data such as clinical notes. To optimally predict SHoSI models need to be tested and validated separately for those young people with varying degrees of risk.
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Affiliation(s)
- Gayani Tennakoon
- Institute for Social Science Research, University of Queensland, Brisbane, Indooroopilly, Australia
- Centre for Social Data Analytics, Auckland University of Technology, Auckland, New Zealand
| | - Enda M Byrne
- Child Health Research Centre, University of Queensland, Brisbane, Queensland, Australia
| | - Rhema Vaithianathan
- Institute for Social Science Research, University of Queensland, Brisbane, Indooroopilly, Australia
- Centre for Social Data Analytics, Auckland University of Technology, Auckland, New Zealand
| | - Christel M Middeldorp
- Child Health Research Centre, University of Queensland, Brisbane, Queensland, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, Brisbane, Queensland, Australia
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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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Haghish EF, Czajkowski NO, von Soest T. Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach. Front Psychiatry 2023; 14:1216791. [PMID: 37822798 PMCID: PMC10562596 DOI: 10.3389/fpsyt.2023.1216791] [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: 05/04/2023] [Accepted: 09/04/2023] [Indexed: 10/13/2023] Open
Abstract
Introduction Research on the classification models of suicide attempts has predominantly depended on the collection of sensitive data related to suicide. Gathering this type of information at the population level can be challenging, especially when it pertains to adolescents. We addressed two main objectives: (1) the feasibility of classifying adolescents at high risk of attempting suicide without relying on specific suicide-related survey items such as history of suicide attempts, suicide plan, or suicide ideation, and (2) identifying the most important predictors of suicide attempts among adolescents. Methods Nationwide survey data from 173,664 Norwegian adolescents (ages 13-18) were utilized to train a binary classification model, using 169 questionnaire items. The Extreme Gradient Boosting (XGBoost) algorithm was fine-tuned to classify adolescent suicide attempts, and the most important predictors were identified. Results XGBoost achieved a sensitivity of 77% with a specificity of 90%, and an AUC of 92.1% and an AUPRC of 47.1%. A coherent set of predictors in the domains of internalizing problems, substance use, interpersonal relationships, and victimization were pinpointed as the most important items related to recent suicide attempts. Conclusion This study underscores the potential of machine learning for screening adolescent suicide attempts on a population scale without requiring sensitive suicide-related survey items. Future research investigating the etiology of suicidal behavior may direct particular attention to internalizing problems, interpersonal relationships, victimization, and substance use.
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Affiliation(s)
- E. F. Haghish
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Nikolai O. Czajkowski
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Division of Mental and Physical Health, Norwegian Institute of Public Health (NIPH), Oslo, Norway
| | - Tilmann von Soest
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
- Norwegian Social Research (NOVA), Oslo Metropolitan University, Oslo, Norway
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11
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Czyz EK, Koo HJ, Al-Dajani N, King CA, Nahum-Shani I. Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization. Psychol Med 2023; 53:2982-2991. [PMID: 34879890 PMCID: PMC9814182 DOI: 10.1017/s0033291721005006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/22/2021] [Accepted: 11/16/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Mobile technology offers unique opportunities for monitoring short-term suicide risk in daily life. In this study of suicidal adolescent inpatients, theoretically informed risk factors were assessed daily following discharge to predict near-term suicidal ideation and inform decision algorithms for identifying elevations in daily level risk, with implications for real-time suicide-focused interventions. METHODS Adolescents (N = 78; 67.9% female) completed brief surveys texted daily for 4 weeks after discharge (n = 1621 observations). Using multi-level classification and regression trees (CARTSs) with repeated 5-fold cross-validation, we tested (a) a simple prediction model incorporating previous-day scores for each of 10 risk factors, and (b) a more complex model incorporating, for each of these factors, a time-varying person-specific mean over prior days together with deviation from that mean. Models also incorporated missingness and contextual (study week, day of the week) indicators. The outcome was the presence/absence of next-day suicidal ideation. RESULTS The best-performing model (cross-validated AUC = 0.86) was a complex model that included ideation duration, hopelessness, burdensomeness, and self-efficacy to refrain from suicidal action. An equivalent model that excluded ideation duration had acceptable overall performance (cross-validated AUC = 0.78). Models incorporating only previous-day scores, with and without ideation duration (cross-validated AUC of 0.82 and 0.75, respectively), showed relatively weaker performance. CONCLUSIONS Results suggest that specific combinations of dynamic risk factors assessed in adolescents' daily life have promising utility in predicting next-day suicidal thoughts. Findings represent an important step in the development of decision tools identifying short-term risk as well as guiding timely interventions sensitive to proximal elevations in suicide risk in daily life.
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Affiliation(s)
- E. K. Czyz
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - H. J. Koo
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - N. Al-Dajani
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - C. A. King
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - I. Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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12
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Lim JS, Yang CM, Baek JW, Lee SY, Kim BN. Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2022; 20:609-620. [PMID: 36263637 PMCID: PMC9606439 DOI: 10.9758/cpn.2022.20.4.609] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/18/2021] [Accepted: 05/27/2021] [Indexed: 01/15/2023]
Abstract
OBJECTIVE Suicide attempts (SAs) in adolescents are difficult to predict although it is a leading cause of death among adolescents. This study aimed to develop and evaluate SA prediction models based on six different machine learning (ML) algorithms for Korean adolescents using data from online surveys. METHODS Data were extracted from the 2011-2018 Korea Youth Risk Behavior Survey (KYRBS), an ongoing annual national survey. The participants comprised 468,482 nationally representative adolescents from 400 middle and 400 high schools, aged 12 to 18. The models were trained using several classic ML methods and then tested on internal and external independent datasets; performance metrics were calculated. Data analysis was performed from March 2020 to June 2020. RESULTS Among the 468,482 adolescents included in the analysis, 15,012 cases (3.2%) were identified as having made an SA. Three features (suicidal ideation, suicide planning, and grade) were identified as the most important predictors. The performance of the six ML models on the internal testing dataset was good, with both the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) ranging from 0.92 to 0.94. Although the AUROC of all models on the external testing dataset (2018 KYRBS) ranged from 0.93 to 0.95, the AUPRC of the models was approximately 0.5. CONCLUSION The developed and validated SA prediction models can be applied to detect high risks of SA. This approach could facilitate early intervention in the suicide crisis and may ultimately contribute to suicide prevention for adolescents.
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Affiliation(s)
- Jae Seok Lim
- Department of Oral and Maxillofacial Surgery, Chungbuk National University Hospital, Cheongju, Korea
| | - Chan-Mo Yang
- Department of Psychiatry, School of Medicine, Wonkwang University, Iksan, Korea,Division of Child and Adolescent Psychiatry, Department of Psychiatry, Graduate School of Medicine, Seoul National University, Seoul, Korea
| | - Ju-Won Baek
- Dental Clinic Center, Chungbuk National University Hospital, Cheongju, Korea
| | - Sang-Yeol Lee
- Department of Psychiatry, School of Medicine, Wonkwang University, Iksan, Korea,Address for correspondence: Sang-Yeol Lee Department of Psychiatry, School of Medicine, Wonkwang University, 895 Muwang-ro, Iksan 54538, Korea, E-mail: , ORCID: https://orcid.org/0000-0003-1828-9992, Bung-Nyun Kim, Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea, E-mail: , ORCID: https://orcid.org/0000-0002-2403-3291
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Graduate School of Medicine, Seoul National University, Seoul, Korea,Address for correspondence: Sang-Yeol Lee Department of Psychiatry, School of Medicine, Wonkwang University, 895 Muwang-ro, Iksan 54538, Korea, E-mail: , ORCID: https://orcid.org/0000-0003-1828-9992, Bung-Nyun Kim, Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea, E-mail: , ORCID: https://orcid.org/0000-0002-2403-3291
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13
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Nordin N, Zainol Z, Mohd Noor MH, Chan LF. Suicidal behaviour prediction models using machine learning techniques: A systematic review. Artif Intell Med 2022; 132:102395. [DOI: 10.1016/j.artmed.2022.102395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 08/12/2022] [Accepted: 08/29/2022] [Indexed: 11/02/2022]
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14
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Dai Z, Zhou H, Zhang W, Tang H, Wang T, Chen Z, Yao Z, Lu Q. Alpha-beta decoupling relevant to inhibition deficits leads to suicide attempt in major depressive disorder. J Affect Disord 2022; 314:168-175. [PMID: 35820473 DOI: 10.1016/j.jad.2022.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/30/2022] [Accepted: 07/07/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND One devastating outcome of major depressive disorder (MDD) is high suicidality, especially for patients with suicide attempt (SA). Evidence indicated that SA may be strongly associated with inhibitory control deficits. We hypothesized that the inhibition function deficits of patient with SA might be underpinned by abnormal neuronal oscillations. METHODS Our study recruited 111 subjects including 74 patients and 37 controls, who performed a GO/NOGO task during magnetoencephalography recording. Time-frequency-representations and phase-amplitude-coupling were measured for the brain circuits involved in the inhibitory function. Phase-slope-indexes were calculated between regions to determine the direction of power flow. RESULTS Significant increased reaction time and decreased judgment accuracy were observed in SA group. During the perception stage of GO task (approximately 125 ms), SA group manifested elevated alpha power in ventral prefrontal cortex (VPFC) and attenuated beta power in dorsal anterior cingulate (dACC) compared with other groups (p < 0.01). In the processing stage of NOGO task (approximately 300 ms), they showed decreased beta power in VPFC and increased alpha power in dACC (p < 0.01). Alpha-beta decoupling during both tasks was observed in SA group. Furthermore, the decoupling from VPFC to dACC under NOGO tasks was significantly correlated with suicide risk level. LIMITATIONS The number of participants was relatively small, and psychological elements were not involved in current study. CONCLUSION Dysregulated oscillatory activities of dACC and VPFC suggested deficits in execution and inhibition functions triggering high suicide risks. The alpha-beta decoupling from VPFC to dACC could be served as a neuro-electrophysiological biomarker for identifying potential suicide risk.
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Affiliation(s)
- Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing 210096, China
| | - Hongliang Zhou
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Wei Zhang
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing 210096, China
| | - Hao Tang
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Ting Wang
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing 210096, China
| | - Zhilu Chen
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Zhijian Yao
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing 210096, China; Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing 210096, China.
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15
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van Velzen LS, Toenders YJ, Kottaram A, Youzchalveen B, Pilkington V, Cotton SM, Brooker A, McKechnie B, Rice S, Schmaal L. Risk Factors for Suicide Attempt During Outpatient Care in Adolescents With Severe and Complex Depression. CRISIS 2022. [PMID: 35548884 DOI: 10.1027/0227-5910/a000860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Background: Young people receiving tertiary mental health care are at elevated risk for suicidal behavior, and understanding which individuals are at increased risk during care is important for treatment and suicide prevention. Aim: We aimed to retrospectively identify risk factors for attempted suicide during outpatient care and predict which young people did or did not attempt during care. Method: Penalized logistic regression analysis was performed in a small high-risk sample of 84 young people receiving care at Orygen's Youth Mood Clinic (age: 14-25 years, 51% female) to predict suicide attempt during care (N = 16). Results: Prediction of suicide attempt during care was only moderately accurate (Area Under the Receiver Operating Curve range 0.71; sensitivity 0.57) using a combination of sociodemographic, psychosocial, and clinical variables. The features that best discriminated both groups included suicidal ideation during care, history of suicide attempt prior to care, changes in appetite reported on the PHQ-9, history of parental separation, and parental mental illness. Limitation: Replication of findings in an independent validation sample is needed. Conclusion: While prediction of suicide attempt during care was only moderately successful, we were able to identify individual risk factors for suicidal behavior during care in a high-risk sample.
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Affiliation(s)
- Laura S van Velzen
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
| | - Yara J Toenders
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
| | - Akhil Kottaram
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
| | - Belinsha Youzchalveen
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
| | - Vita Pilkington
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
| | - Sue M Cotton
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
| | - Abi Brooker
- School of Psychological Sciences, University of Melbourne, VIC, Australia
| | | | - Simon Rice
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
| | - Lianne Schmaal
- Orygen, Parkville, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, VIC, Australia
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16
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Gupta M, Ramar D, Vijayan R, Gupta N. Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults. ADOLESCENT PSYCHIATRY 2022. [DOI: 10.2174/2210676612666220408095913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Artificial Intelligence is making a significant transformation in human lives. Its application in the medical and healthcare field has been also observed making an impact and improving overall outcomes. There has been a quest for similar processes in mental health due to the lack of observable changes in the areas of suicide prevention. In the last five years, there has been an emerging body of empirical research applying the technology of artificial intelligence (AI) and machine learning (ML) in mental health.
Objective:
To review the clinical applicability of the AI/ML-based tools in suicide prevention.
Methods:
The compelling question of predicting suicidality has been the focus of this research.
We performed a broad literature search and then identified 36 articles relevant to meet the objectives of this review. We review the available evidence and provide a brief overview of the advances in this field.
Conclusion:
In the last five years, there has been more evidence supporting the implementation of these algorithms in clinical practice. Its current clinical utility is limited to using electronic health records and could be highly effective in conjunction with existing tools for suicide prevention. Other potential sources of relevant data include smart devices and social network sites. There are some serious questions about data privacy and ethics which need more attention while developing these new modalities in suicide research.
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Affiliation(s)
| | - Dhanvendran Ramar
- Bellin Health Psychiatric Clinical Services, & Medical College of Wisconsin Green Bay Wisconsin 54301
| | - Rekha Vijayan
- Bellin Health Psychiatric Clinical Services, & Medical College of Wisconsin Green Bay Wisconsin 54301
| | - Nihit Gupta
- University of West Virginia, Reynolds Memorial Hospital Glendale WV 26038
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17
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A Panorama of the Medicolegal Aspects of Suicide Assessments: Integrating Multiple Vantage Points in Improving Quality, Safety, and Risk Management. CNS Spectr 2022; 28:282-287. [PMID: 35383552 DOI: 10.1017/s1092852922000724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Epidemiological trends in global suicides have been of serious concern in the last decade. The burden of higher expectations in the assessment of suicidal behaviors on mental health professionals is mounting. However, the suicidal risk assessment has many evolving and moving parts, and is one of the most heavily researched fields in psychiatry. Although it is clear from current empirical research that suicide cannot accurately be predicted, the standard of care from regulatory bodies and professional organizations dictates the use of established measures and following consensus guidelines. However, the legal system has different parameters to assess for the deviation from these standards and views it from a different vantage point. Therefore, it is imperative to know these critical multifaceted panoramas of suicide assessment. Considering the gaps within suicide risk assessment tools, we propose that appropriate documentation and thorough treatment planning are key to navigating the complex medicolegal risks. These approaches are useful for risk management and improve clinical outcomes, quality of care, and overall patient safety.
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18
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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19
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Lejeune A, Le Glaz A, Perron PA, Sebti J, Baca-Garcia E, Walter M, Lemey C, Berrouiguet S. Artificial intelligence and suicide prevention: a systematic review. Eur Psychiatry 2022; 65:1-22. [PMID: 35166203 PMCID: PMC8988272 DOI: 10.1192/j.eurpsy.2022.8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 11/23/2022] Open
Abstract
Background Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide. Methods A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords. Results Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings. Conclusions AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified.
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Affiliation(s)
- Alban Lejeune
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | - Aziliz Le Glaz
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | | | - Johan Sebti
- Mental Health Department, French Polynesia Hospital, FFC3+H9G, Pirae, French Polynesia
| | | | - Michel Walter
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
| | - Christophe Lemey
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
- SPURBO, IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, Brest, France
| | - Sofian Berrouiguet
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Brest, France
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20
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Wei YX, Liu BP, Zhang J, Wang XT, Chu J, Jia CX. Prediction of recurrent suicidal behavior among suicide attempters with Cox regression and machine learning: a 10-year prospective cohort study. J Psychiatr Res 2021; 144:217-224. [PMID: 34700209 DOI: 10.1016/j.jpsychires.2021.10.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/28/2021] [Accepted: 10/18/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Research on predictors and risk of recurrence after suicide attempt from China is lacking. This study aims to identify risk factors and develop prediction models for recurrent suicidal behavior among suicide attempters using Cox proportional hazard (CPH) and machine learning methods. METHODS The prospective cohort study included 1103 suicide attempters with a maximum follow-up of 10 years from rural China. Baseline characteristics, collected by face-to-face interviews at least 1 month later after index suicide attempt, were used to predict recurrent suicidal behavior. CPH and 3 machine learning algorithms, namely, the least absolute shrinkage and selection operator, random survival forest, and gradient boosting decision tree, were used to construct prediction models. Model performance was accessed by concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve (AUC) value for discrimination, and time-dependent calibration curve along with Brier score for calibration. RESULTS The median follow-up time was 7.79 years, and 49 suicide attempters had recurrent suicidal behavior during the study period. Four models achieved comparably good discrimination and calibration performance, with all C-indexes larger than 0.70, AUC values larger than 0.65, and Brier scores smaller than 0.06. Mental disorder emerged as the most important predictor across all four models. Suicide attempters with mental disorders had a 3 times higher risk of recurrence than those without. History of suicide attempt (HR = 2.84, 95% CI: 1.34-6.02), unstable marital status (HR = 2.81, 95% CI: 1.38-5.71), and older age (HR = 1.51, 95% CI: 1.14-2.01) were also identified as independent predictors of recurrent suicidal behavior by CPH model. CONCLUSIONS We developed four models to predict recurrent suicidal behavior with comparable good prediction performance. Our findings potentially provided benefits in screening vulnerable individuals on a more precise scale.
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Affiliation(s)
- Yan-Xin Wei
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Bao-Peng Liu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Jie Zhang
- Shandong University Center for Suicide Prevention Research, China; Department of Sociology, State University of New York College at Buffalo, Buffalo, NY, 14222, USA
| | - Xin-Ting Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Jie Chu
- Shandong Center for Disease Prevention and Control, Jinan, 250014, Shandong, China
| | - Cun-Xian Jia
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China.
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21
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Ballester PL, Cardoso TDA, Moreira FP, da Silva RA, Mondin TC, Araujo RM, Kapczinski F, Frey BN, Jansen K, de Mattos Souza LD. 5-year incidence of suicide-risk in youth: A gradient tree boosting and SHAP study. J Affect Disord 2021; 295:1049-1056. [PMID: 34706413 DOI: 10.1016/j.jad.2021.08.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 07/15/2021] [Accepted: 08/21/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Machine learning methods for suicidal behavior so far have failed to be implemented as a prediction tool. In order to use the capabilities of machine learning to model complex phenomenon, we assessed the predictors of suicide risk using state-of-the-art model explanation methods. METHODS Prospective cohort study including a community sample of 1,560 young adults aged between 18 and 24. The first wave took place between 2007 and 2009, and the second wave took place between 2012 and 2014. Sociodemographic and clinical characteristics were assessed at baseline. Incidence of suicide risk at five-years of follow-up was the main outcome. The outcome was assessed using the Mini Neuropsychiatric Interview (MINI) at both waves. RESULTS The risk factors for the incidence of suicide risk at follow-up were: female sex, lower socioeconomic status, older age, not studying, presence of common mental disorder symptoms, and poor quality of life. The interaction between overall health and socioeconomic status in relation to suicide risk was also captured and shows a shift from protection to risk by socioeconomic status as overall health increases. LIMITATIONS Proximal factors associated with the incidence of suicide risk were not assessed. CONCLUSIONS Our findings indicate that factors related to poor quality of life, not studying, and common mental disorder symptoms of young adults are already in place prior to suicide risk. Most factors present critical non-linear patterns that were identified. These findings are clinically relevant because they can help clinicians to early detect suicide risk.
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Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Taiane de A Cardoso
- Department of Health and Behavior, Catholic University of Pelotas, Pelotas, RS, Brazil; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil; Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Fernanda Pedrotti Moreira
- Department of Health and Behavior, Catholic University of Pelotas, Pelotas, RS, Brazil; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil
| | - Ricardo A da Silva
- Department of Health and Behavior, Catholic University of Pelotas, Pelotas, RS, Brazil; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil
| | - Thaíse Campos Mondin
- Department of Student Affairs, Federal University of Pelotas, Pelotas, RS, Brazil
| | - Ricardo M Araujo
- Center for Technological Development, Federal University of Pelotas, Pelotas, RS, Brazil
| | - Flavio Kapczinski
- Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil; Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Bipolar Disorder Program, Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Instituto Nacional de Ciência e Tecnologia Translacional em Medicina, Porto Alegre, RS, Brazil; Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Benicio N Frey
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Karen Jansen
- Department of Health and Behavior, Catholic University of Pelotas, Pelotas, RS, Brazil; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil
| | - Luciano D de Mattos Souza
- Department of Health and Behavior, Catholic University of Pelotas, Pelotas, RS, Brazil; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil.
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22
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Clayton MG, Pollak OH, Owens SA, Miller AB, Prinstein MJ. Advances in Research on Adolescent Suicide and a High Priority Agenda for Future Research. JOURNAL OF RESEARCH ON ADOLESCENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR RESEARCH ON ADOLESCENCE 2021; 31:1068-1096. [PMID: 34820949 DOI: 10.1111/jora.12614] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Suicide is the second leading cause of death for adolescents in the United States, yet remarkably little is known regarding risk factors for suicidal thoughts and behaviors (STBs), relatively few federal grants and scientific publications focus on STBs, and few evidence-based approaches to prevent or treat STBs are available. This "decade in review" article discusses five domains of recent empirical findings that span biological, environmental, and contextual systems and can guide future research in this high priority area: (1) the role of the central nervous system; (2) physiological risk factors, including the peripheral nervous system; (3) proximal acute stress responses; (4) novel behavioral and psychological risk factors; and (5) broader societal factors impacting diverse populations and several additional nascent areas worthy of further investigation.
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23
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Grendas LN, Chiapella L, Rodante DE, Daray FM. Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour. J Psychiatr Res 2021; 145:85-91. [PMID: 34883411 DOI: 10.1016/j.jpsychires.2021.11.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 10/23/2021] [Accepted: 11/20/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND Despite considerable research efforts during the last five decades, the prediction of suicidal behaviour (SB) using traditional model-based statistical has been weak. This marks the need to explore new statistical methods. OBJECTIVE To compare the performance of Cox regression models versus Random Survival Forest (RSF) to predict SB. METHODS Using a data set of more than 300 high-risk suicidal patients from a multicenter prospective cohort study, we compare Cox regression models with RSF to address predictors of time to suicide reattempt. Cross-validation was used to assess model prediction performance, including the area under the receiver operator curve (AUC), precision, Integrated Brier Score (IBS), sensitivity, and specificity. RESULTS A variant of the RSF denominated the RSFElimin, in which irrelevant predictor variables were eliminated from the model, presented the best accuracy, sensitivity, AUC and IBS. At the same time, the sensitivity of this method was slightly lower than that obtained with the Cox regression model with all predictor variables (CoxComp). CONCLUSION The RSF, a machine learning model, seems more sensitive and precise than the traditional Cox regression model in predicting suicidal behaviour.
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Affiliation(s)
- Leandro Nicolás Grendas
- University of Buenos Aires, School of Medicine, Institute of Pharmacology, Argentina; Teodoro Alvarez Hospital, Buenos Aires, Argentina
| | - Luciana Chiapella
- National University of Rosario, School of Biochemical and Pharmaceutical Sciences, Argentina; National Scientific and Technical Research Council (CONICET), Argentina
| | - Demian Emanuel Rodante
- University of Buenos Aires, School of Medicine, Institute of Pharmacology, Argentina; Braulio A. Moyano Neuropsychiatric Hospital, Buenos Aires, Argentina
| | - Federico Manuel Daray
- University of Buenos Aires, School of Medicine, Institute of Pharmacology, Argentina; National Scientific and Technical Research Council (CONICET), Argentina.
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24
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Penfold RB, Johnson E, Shortreed SM, Ziebell RA, Lynch FL, Clarke GN, Coleman KJ, Waitzfelder BE, Beck AL, Rossom RC, Ahmedani BK, Simon GE. Predicting suicide attempts and suicide deaths among adolescents following outpatient visits. J Affect Disord 2021; 294:39-47. [PMID: 34265670 PMCID: PMC8820270 DOI: 10.1016/j.jad.2021.06.057] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 06/21/2021] [Accepted: 06/27/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation.
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Affiliation(s)
- Robert B. Penfold
- Kaiser Permanente Washington Health Research Institute,Corresponding author Robert Penfold, 1730 Minor Ave, Suite 1600, Seattle, WA 98101, (206) 287-2232 voice, (206) 287-2871 fax,
| | - Eric Johnson
- Kaiser Permanente Washington Health Research Institute
| | | | | | | | | | - Karen J. Coleman
- Kaiser Permanente Southern California Department of Research and Evaluation
| | | | - Arne L. Beck
- Kaiser Permanente Colorado Institute for Health Research
| | | | - Brian K. Ahmedani
- Henry Ford Health System, Center for Health Policy & Health Services Research
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25
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Penfold RB, Whiteside U, Johnson EE, Stewart CC, Oliver MM, Shortreed SM, Beck A, Coleman KJ, Rossom RC, Lawrence JM, Simon GE. Utility of item 9 of the patient health questionnaire in the prospective identification of adolescents at risk of suicide attempt. Suicide Life Threat Behav 2021; 51:854-863. [PMID: 34331466 DOI: 10.1111/sltb.12751] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/11/2020] [Accepted: 01/13/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Previous studies report that item 9 of the Patient Health Questionnaire (PHQ9) is useful for stratifying risk of suicide attempt in adults. This study re-produced the utility of item 9 of PHQ9 in assessing risk of suicide attempt in adolescents. MATERIALS AND METHODS Individuals aged 13 to 17 years in 4 health systems with a diagnosis of depression and history of treatment were included. We estimated time to first observed fatal or non-fatal suicide attempt in the 2 years following completion of a PHQ9, stratified by response to item 9. RESULTS There were 51,807 PHQ9 questionnaires for 20,363 youth and 861 instances of suicide attempt. Cumulative probability of suicide attempt ranged from approximately 3.3% (95% CI, 3.0 to 3.5%) for those responding "not at all" on item 9 to 10.8% (95% CI, 9.2 to 12.4%) for those responding "nearly every day". These probabilities are more than 3 times higher than previously reported in adults. CONCLUSION PHQ item 9 is useful for stratifying risk of suicide attempt in the 2 years following completion of the questionnaire. Monitoring PHQ item 9 over time for patients in treatment for depression can be useful for population health management of adolescents with depression.
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Affiliation(s)
- Robert B Penfold
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Ursula Whiteside
- Seattle, Washington, USA.,University of Washington, Department of Psychiatry and Behavioral Sciences, Seattle, Washington, USA
| | - Eric E Johnson
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Christine C Stewart
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Malia M Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Susan M Shortreed
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Arne Beck
- Kaiser Permanente Colorado Institute for Health Research, Denver, Colorado, USA
| | - Karen J Coleman
- Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena, CA, USA
| | | | - Jean M Lawrence
- Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena, CA, USA
| | - Gregory E Simon
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
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26
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Ji X, Zhao J, Fan L, Li H, Lin P, Zhang P, Fang S, Law S, Yao S, Wang X. Highlighting psychological pain avoidance and decision-making bias as key predictors of suicide attempt in major depressive disorder-A novel investigative approach using machine learning. J Clin Psychol 2021; 78:671-691. [PMID: 34542183 DOI: 10.1002/jclp.23246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/05/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Predicting suicide is notoriously difficult and complex, but a serious public health issue. An innovative approach utilizing machine learning (ML) that incorporates features of psychological mechanisms and decision-making characteristics related to suicidality could create an improved model for identifying suicide risk in patients with major depressive disorder (MDD). METHOD Forty-four patients with MDD and past suicide attempts (MDD_SA, N = 44); 48 patients with MDD but without past suicide attempts (MDD_NS, N = 48-42 of whom with suicide ideation [MDD_SI, N = 42]), and healthy controls (HCs, N = 51) completed seven psychometric assessments including the Three-dimensional Psychological Pain Scale (TDPPS), and one behavioral assessment, the Balloon Analogue Risk Task (BART). Descriptive statistics, group comparisons, logistic regressions, and ML were used to explore and compare the groups and generate predictors of suicidal acts. RESULTS MDD_SA and MDD_NS differed in TDPPS total score, pain arousal and avoidance subscale scores, suicidal ideation scores, and relevant decision-making indicators in BART. Logistic regression tests linked suicide attempts to psychological pain avoidance and a risk decision-making indicator. The resultant key ML model distinguished MDD_SA/MDD_NS with 88.2% accuracy. The model could also distinguish MDD_SA/MDD_SI with 81.25% accuracy. The ML model using hopelessness could classify MDD_SI/HC with 94.4% accuracy. CONCLUSION ML analyses showed that motivation to avoid intolerable psychological pain, coupled with impaired decision-making bias toward under-valuing life's worth are highly predictive of suicide attempts. Analyses also demonstrated that suicidal ideation and attempts differed in potential mechanisms, as suicidal ideation was more related to hopelessness. ML algorithms show useful promises as a predictive instrument.
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Affiliation(s)
- Xinlei Ji
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jiahui Zhao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lejia Fan
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Huanhuan Li
- Department of Psychology, Renmin University of China, Beijing, China
| | - Pan Lin
- Department of Psychology and Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, Hunan, China
| | - Panwen Zhang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shulin Fang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Samuel Law
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Shuqiao Yao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,Medical Psychological Institute of Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,Medical Psychological Institute of Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
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27
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Mansourian M, Khademi S, Marateb HR. A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining. Diagnostics (Basel) 2021; 11:393. [PMID: 33669114 PMCID: PMC7996506 DOI: 10.3390/diagnostics11030393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/13/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023] Open
Abstract
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.
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Affiliation(s)
- Mahsa Mansourian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran;
| | - Sadaf Khademi
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
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28
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Arango A, Gipson PY, Votta JG, King CA. Saving Lives: Recognizing and Intervening with Youth at Risk for Suicide. Annu Rev Clin Psychol 2021; 17:259-284. [PMID: 33544628 DOI: 10.1146/annurev-clinpsy-081219-103740] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Suicide is the second leading cause of death for youth in the United States. Fortunately, substantial advances have been achieved in identifying and intervening with youth at risk. In this review, we first focus on advances in proactive suicide risk screening and psychoeducation aimed at improving the recognition of suicide risk. These strategies have the potential to improve our ability to recognize and triage youth at risk who may otherwise be missed. We then review recent research on interventions for youth at risk. We consider a broad range of psychotherapeutic interventions, including crisis interventions in emergency care settings. Though empirical support remains limited for interventions targeting suicide risk in youth, effective and promising approaches continue to be identified. We highlight evidence-based screening and intervention approaches as well as challenges in these areas and recommendations for further investigation.
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Affiliation(s)
- Alejandra Arango
- Department of Psychiatry, Michigan Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA;
| | - Polly Y Gipson
- Department of Psychiatry, Michigan Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA;
| | - Jennifer G Votta
- Department of Psychiatry, Michigan Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA;
| | - Cheryl A King
- Department of Psychiatry, Michigan Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA;
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29
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Lin S, Wu Y, Fang Y. Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study. Front Psychiatry 2021; 12:764806. [PMID: 35111085 PMCID: PMC8801448 DOI: 10.3389/fpsyt.2021.764806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/26/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Depression is highly prevalent and considered as the most common psychiatric disorder in home-based elderly, while study on forecasting depression risk in the elderly is still limited. In an endeavor to improve accuracy of depression forecasting, machine learning (ML) approaches have been recommended, in addition to the application of more traditional regression approaches. METHODS A prospective study was employed in home-based elderly Chinese, using baseline (2011) and follow-up (2013) data of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study. We compared four algorithms, including the regression-based models (logistic regression, lasso, ridge) and ML method (random forest). Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance, we used the area under the receiver operating characteristic curve (AUC). RESULTS The mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.795, 0.794, 0.794, and 0.769, respectively. The main determinants were life satisfaction, self-reported memory, cognitive ability, ADL (activities of daily living) impairment, CESD-10 score. Life satisfaction increased the odds ratio of a future depression by 128.6% (logistic), 13.8% (lasso), and 13.2% (ridge), and cognitive ability was the most important predictor in random forest. CONCLUSIONS The three regression-based models and one ML algorithm performed equally well in differentiating between a future depression case and a non-depression case in home-based elderly. When choosing a model, different considerations, however, such as easy operating, might in some instances lead to one model being prioritized over another.
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Affiliation(s)
- Shaowu Lin
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Yafei Wu
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
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