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Khosravi H, Ahmed I, Choudhury A. Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States. Healthcare (Basel) 2024; 12:1262. [PMID: 38998797 PMCID: PMC11241284 DOI: 10.3390/healthcare12131262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/20/2024] [Accepted: 06/22/2024] [Indexed: 07/14/2024] Open
Abstract
Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment.
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Affiliation(s)
- Hamed Khosravi
- Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Imtiaz Ahmed
- Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Avishek Choudhury
- Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV 26506, USA
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Chen G, Liu Y, Mao Y. Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol. PLoS One 2024; 19:e0304109. [PMID: 38781194 PMCID: PMC11115232 DOI: 10.1371/journal.pone.0304109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
With the advancement of computer-based testing, log file data has drawn considerable attention from researchers. Although emerging studies have begun to explore log file data, there is a gap in the exploitation of log file data for capturing and understanding participants' cognitive processes. The debate on how to maximize insights from log file data has not yet reached a consensus. Therefore, we present this protocol for a scoping review that aims to characterize the application of log file data in current publications, including the data pre-processing techniques, analytical methodologies, and theoretical frameworks used by researchers. This review will also aim to illuminate how log file data can enhance psychological and educational assessments. Our findings will highlight the opportunities and challenges presented by log file data as an emerging and essential source of evidence for future advancements in psychological and educational assessment.
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Affiliation(s)
- Guanyu Chen
- Department of Educational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, Canada
| | - Yan Liu
- Department of Psychology, Carleton University, Ottawa, Canada
| | - Yue Mao
- Department of Educational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, Canada
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McCool MW, Schwebel FJ, Pearson MR, Wong MM. Using recursive partitioning to predict presence and severity of suicidal ideation amongst college students. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2024:1-11. [PMID: 38728739 DOI: 10.1080/07448481.2024.2351419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024]
Abstract
OBJECTIVE Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years. PARTICIPANTS We recruited college students (N = 5494) from ten universities across eight states. METHOD Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive partitioning to predict the presence, absence, and severity, of suicidal ideation. RESULTS Recursive partitioning models varied in their accuracy and performance. The best-performing model consisted of predictors and outcomes measured by the DSM-5 Level 1 Cross-Cutting Symptom Measure. Sexual orientation was also an important predictor in most models. CONCLUSIONS A single measure of DSM-5 symptom severity may help universities understand suicide severity to promote targeted interventions. Though further work is needed, as similar scaling amongst predictors could have influenced the model.
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Affiliation(s)
- Matison W McCool
- Center on Alcohol, Substance use, and Addictions, University of New Mexico, Albuquerque, New Mexico, USA
| | - Frank J Schwebel
- Center on Alcohol, Substance use, and Addictions, University of New Mexico, Albuquerque, New Mexico, USA
| | - Matthew R Pearson
- Center on Alcohol, Substance use, and Addictions, University of New Mexico, Albuquerque, New Mexico, USA
| | - Maria M Wong
- Psychology Department, Idaho State University, Pocatello, Idaho, USA
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Lissak S, Ophir Y, Tikochinski R, Brunstein Klomek A, Sisso I, Fruchter E, Reichart R. Bored to death: Artificial Intelligence research reveals the role of boredom in suicide behavior. Front Psychiatry 2024; 15:1328122. [PMID: 38784160 PMCID: PMC11112344 DOI: 10.3389/fpsyt.2024.1328122] [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: 10/26/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Background Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Methods The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusion Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.
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Affiliation(s)
- Shir Lissak
- The Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Haifa, Israel
| | - Yaakov Ophir
- The Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Haifa, Israel
- The Centre for Human-Inspired Artificial Intelligence (CHIA), University of Cambridge, Cambridge, United Kingdom
| | - Refael Tikochinski
- The Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Haifa, Israel
| | | | - Itay Sisso
- Cognitive Science Department, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Eyal Fruchter
- Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - Roi Reichart
- The Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Haifa, Israel
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Yang C, Huebner ES, Tian L. Prediction of suicidal ideation among preadolescent children with machine learning models: A longitudinal study. J Affect Disord 2024; 352:403-409. [PMID: 38387673 DOI: 10.1016/j.jad.2024.02.070] [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: 08/19/2023] [Revised: 02/15/2024] [Accepted: 02/19/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Machine learning (ML) has been widely used to predict suicidal ideation (SI) in adolescents and adults. Nevertheless, studies of accurate and efficient models of SI prediction with preadolescent children are still needed because SI is surprisingly prevalent during the transition into adolescence. This study aimed to explore the potential of ML models to predict SI among preadolescent children. METHODS A total of 4691 Chinese children (54.89 % boys, Mage = 10.92 at baseline) and their parents completed relevant measures at baseline and the children provided 6-month follow-up data for SI. The current study compared four ML models: Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), to predict SI and to identify variables with predictive value based on the best-performing model among Chinese preadolescent children. RESULTS The RF model achieved the highest discriminant performance with an AUC of 0.92, accuracy of 0.93 (balanced accuracy = 0.88). The factors of internalizing problems, externalizing problems, neuroticism, childhood maltreatment, and subjective well-being in school demonstrated the highest values in predicting SI. CONCLUSION The findings of this study suggested that ML models based on the observation and assessment of children's general characteristics and experiences in everyday life can serve as convenient screening and evaluation tools for suicide risk assessment among Chinese preadolescent children. The findings also provide insights for early intervention.
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Affiliation(s)
- Chi Yang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, People's Republic of China; School of Psychology, South China Normal University, Guangzhou 510631, People's Republic of China
| | - E Scott Huebner
- Department of Psychology, University of South Carolina, Columbia, SC 29208, USA
| | - Lili Tian
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, People's Republic of China.
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Tio ES, Misztal MC, Felsky D. Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning. Front Psychiatry 2024; 14:1294666. [PMID: 38274429 PMCID: PMC10808719 DOI: 10.3389/fpsyt.2023.1294666] [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: 09/15/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024] Open
Abstract
Background Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide. Methods We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide. Results Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance. Conclusion Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.
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Affiliation(s)
- Earvin S. Tio
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Melissa C. Misztal
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Alanazi SA, Khaliq A, Ahmad F, Alshammari N, Hussain I, Zia MA, Alruwaili M, Rayan A, Alsayat A, Afsar S. Public's Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19159695. [PMID: 35955051 PMCID: PMC9368160 DOI: 10.3390/ijerph19159695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 05/03/2023]
Abstract
Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public's mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public's mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.
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Affiliation(s)
- Saad Awadh Alanazi
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
- Correspondence:
| | - Ayesha Khaliq
- Department of Computer Science, National Textile University, Faisalabad 37300, Pakistan
- Department of Computer Science, University of Agriculture Faisalabad, Faisalabad 37300, Pakistan
| | - Fahad Ahmad
- Department of Basic Sciences, Deanship of Common First Year, Jouf University, Sakaka 72341, Saudi Arabia
| | - Nasser Alshammari
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
| | - Iftikhar Hussain
- Center for Sustainable Road Freight and Business Management, Heriot-Watt University, Edinburgh EH14 4AS, UK
| | - Muhammad Azam Zia
- Department of Computer Science, University of Agriculture Faisalabad, Faisalabad 37300, Pakistan
| | - Madallah Alruwaili
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
| | - Alanazi Rayan
- Department of Computer Science, College of Science and Arts, Jouf University, Qurayyat 77413, Saudi Arabia
| | - Ahmed Alsayat
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
| | - Salman Afsar
- Department of Computer Science, University of Agriculture Faisalabad, Faisalabad 37300, Pakistan
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Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation. Healthcare (Basel) 2022; 10:healthcare10040698. [PMID: 35455874 PMCID: PMC9029735 DOI: 10.3390/healthcare10040698] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/15/2022] [Accepted: 04/05/2022] [Indexed: 12/01/2022] Open
Abstract
People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users’ smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.
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