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Cossu G, Vecchio A, Orlandi M, Casini E, Borgatti R, Mensi MM. Multiphasic Personality Assessment in a Case Series of Adolescent Patients with Suicidal Ideation and/or Attempts. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1794. [PMID: 38002885 PMCID: PMC10670834 DOI: 10.3390/children10111794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
Suicide is an important public health issue. To examine the differences in personality characteristics between a group of adolescents with suicidal ideation (SI) and a group with a history of suicidal attempts (SA), we conducted a cross-sectional study. We enrolled 55 adolescents (51 females; 12-18 y.o.) who presented SI and/or SA. Using the Columbia Suicide Severity Rating Scale, we divided the sample into two groups: adolescents with SI and adolescents with SA. All participants filled in the Minnesota Multiphasic Personality Inventory-Adolescent (MMPI-A). Adolescents in the SA group had greater difficulties in social relations, risky behaviors, and more intense suicidal ideation compared to those in the SI group. Adolescents in the SA group scored higher in Omission, in the Lie Scale, the Conduct Problem Scale, the Less Aspirations Scale, the Repression Scale in the MMPI-A, and item 283 of the MAST compared to the other group. The results suggest that using the MMPI-A to assess certain features (e.g., tendency to lie, repression) may be helpful in identifying young people who are at high risk of suicide. However, further research is required to determine the effectiveness of using this instrument.
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Affiliation(s)
- Giulia Cossu
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy
| | - Arianna Vecchio
- Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Marika Orlandi
- Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Erica Casini
- Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Renato Borgatti
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy
- Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Martina Maria Mensi
- Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
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Levkovich I, Elyoseph Z. Suicide Risk Assessments Through the Eyes of ChatGPT-3.5 Versus ChatGPT-4: Vignette Study. JMIR Ment Health 2023; 10:e51232. [PMID: 37728984 PMCID: PMC10551796 DOI: 10.2196/51232] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND ChatGPT, a linguistic artificial intelligence (AI) model engineered by OpenAI, offers prospective contributions to mental health professionals. Although having significant theoretical implications, ChatGPT's practical capabilities, particularly regarding suicide prevention, have not yet been substantiated. OBJECTIVE The study's aim was to evaluate ChatGPT's ability to assess suicide risk, taking into consideration 2 discernable factors-perceived burdensomeness and thwarted belongingness-over a 2-month period. In addition, we evaluated whether ChatGPT-4 more accurately evaluated suicide risk than did ChatGPT-3.5. METHODS ChatGPT was tasked with assessing a vignette that depicted a hypothetical patient exhibiting differing degrees of perceived burdensomeness and thwarted belongingness. The assessments generated by ChatGPT were subsequently contrasted with standard evaluations rendered by mental health professionals. Using both ChatGPT-3.5 and ChatGPT-4 (May 24, 2023), we executed 3 evaluative procedures in June and July 2023. Our intent was to scrutinize ChatGPT-4's proficiency in assessing various facets of suicide risk in relation to the evaluative abilities of both mental health professionals and an earlier version of ChatGPT-3.5 (March 14 version). RESULTS During the period of June and July 2023, we found that the likelihood of suicide attempts as evaluated by ChatGPT-4 was similar to the norms of mental health professionals (n=379) under all conditions (average Z score of 0.01). Nonetheless, a pronounced discrepancy was observed regarding the assessments performed by ChatGPT-3.5 (May version), which markedly underestimated the potential for suicide attempts, in comparison to the assessments carried out by the mental health professionals (average Z score of -0.83). The empirical evidence suggests that ChatGPT-4's evaluation of the incidence of suicidal ideation and psychache was higher than that of the mental health professionals (average Z score of 0.47 and 1.00, respectively). Conversely, the level of resilience as assessed by both ChatGPT-4 and ChatGPT-3.5 (both versions) was observed to be lower in comparison to the assessments offered by mental health professionals (average Z score of -0.89 and -0.90, respectively). CONCLUSIONS The findings suggest that ChatGPT-4 estimates the likelihood of suicide attempts in a manner akin to evaluations provided by professionals. In terms of recognizing suicidal ideation, ChatGPT-4 appears to be more precise. However, regarding psychache, there was an observed overestimation by ChatGPT-4, indicating a need for further research. These results have implications regarding ChatGPT-4's potential to support gatekeepers, patients, and even mental health professionals' decision-making. Despite the clinical potential, intensive follow-up studies are necessary to establish the use of ChatGPT-4's capabilities in clinical practice. The finding that ChatGPT-3.5 frequently underestimates suicide risk, especially in severe cases, is particularly troubling. It indicates that ChatGPT may downplay one's actual suicide risk level.
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Affiliation(s)
- Inbar Levkovich
- Oranim Academic College, Faculty of Graduate Studies, Kiryat Tivon, Israel
| | - Zohar Elyoseph
- Department of Psychology and Educational Counseling, The Center for Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, Israel
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
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Barrigon ML, Romero-Medrano L, Moreno-Muñoz P, Porras-Segovia A, Lopez-Castroman J, Courtet P, Artés-Rodríguez A, Baca-Garcia E. One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study. J Med Internet Res 2023; 25:e43719. [PMID: 37656498 PMCID: PMC10504627 DOI: 10.2196/43719] [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: 10/21/2022] [Revised: 02/03/2023] [Accepted: 06/26/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. OBJECTIVE We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. METHODS We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. RESULTS During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. CONCLUSIONS We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.
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Affiliation(s)
- Maria Luisa Barrigon
- Department of Psychiatry, Jimenez Diaz Foundation University Hospital, Madrid, Spain
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Lorena Romero-Medrano
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain
- Evidence-Based Behavior (eB2), Madrid, Spain
| | - Pablo Moreno-Muñoz
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain
- Cognitive Systems Section, Technical University of Denmark, Lyngby, Denmark
| | | | - Jorge Lopez-Castroman
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain
- Department of Psychiatry, Centre Hospitalier Universitaire Nîmes, Nîmes, France
- Institut de Génomique Fonctionnelle, CNRS-INSERM, University of Montpellier, Montpellier, France
| | - Philippe Courtet
- Institut de Génomique Fonctionnelle, CNRS-INSERM, University of Montpellier, Montpellier, France
- Department of Emergency Psychiatry and Acute Care, Centre Hospitalier Universitaire, Montpellier, France
| | - Antonio Artés-Rodríguez
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain
- Evidence-Based Behavior (eB2), Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Institute of Health, Madrid, Spain
- Instituto de Investigacion Sanitaria Gregorio Marañón, Madrid, Spain
| | - Enrique Baca-Garcia
- Department of Psychiatry, Jimenez Diaz Foundation University Hospital, Madrid, Spain
- Evidence-Based Behavior (eB2), Madrid, Spain
- Department of Psychiatry, Centre Hospitalier Universitaire Nîmes, Nîmes, France
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Institute of Health, Madrid, Spain
- Department of Psychiatry, Autonomous University of Madrid, Madrid, Spain
- Department of Psychiatry, Rey Juan Carlos University Hospital, Móstoles, Madrid, Spain
- Department of Psychiatry, General Hospital of Villalba, Madrid, Spain
- Department of Psychiatry, Infanta Elena University Hospital, Valdemoro, Madrid, Spain
- Department of Psychology, Universidad Catolica del Maule, Talca, Chile
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Elyoseph Z, Levkovich I. Beyond human expertise: the promise and limitations of ChatGPT in suicide risk assessment. Front Psychiatry 2023; 14:1213141. [PMID: 37593450 PMCID: PMC10427505 DOI: 10.3389/fpsyt.2023.1213141] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/19/2023] [Indexed: 08/19/2023] Open
Abstract
ChatGPT, an artificial intelligence language model developed by OpenAI, holds the potential for contributing to the field of mental health. Nevertheless, although ChatGPT theoretically shows promise, its clinical abilities in suicide prevention, a significant mental health concern, have yet to be demonstrated. To address this knowledge gap, this study aims to compare ChatGPT's assessments of mental health indicators to those of mental health professionals in a hypothetical case study that focuses on suicide risk assessment. Specifically, ChatGPT was asked to evaluate a text vignette describing a hypothetical patient with varying levels of perceived burdensomeness and thwarted belongingness. The ChatGPT assessments were compared to the norms of mental health professionals. The results indicated that ChatGPT rated the risk of suicide attempts lower than did the mental health professionals in all conditions. Furthermore, ChatGPT rated mental resilience lower than the norms in most conditions. These results imply that gatekeepers, patients or even mental health professionals who rely on ChatGPT for evaluating suicidal risk or as a complementary tool to improve decision-making may receive an inaccurate assessment that underestimates the actual suicide risk.
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Affiliation(s)
- Zohar Elyoseph
- Department of Psychology and Educational Counseling, The Center for Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, Israel
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Inbar Levkovich
- Faculty of Graduate Studies, Oranim Academic College, Kiryat Tiv'on, Israel
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Luo Y, Lai Q, Huang H, Luo J, Miao J, Liao R, Yang Z, Zhang L. Risk factor analysis and nomogram construction for predicting suicidal ideation in patients with cancer. BMC Psychiatry 2022; 22:353. [PMID: 35610595 PMCID: PMC9128228 DOI: 10.1186/s12888-022-03987-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/11/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Suicidal ideation in cancer patients is a critical challenge. At present, few studies focus on factors associated with suicidal ideation, and predictive models are still lacking. This study aimed at investigating the risk factors for suicidal ideation among cancer patients, and developed a predictive nomogram to screen high risk cancer patients for early prevention and intervention. METHODS A questionnaire survey was conducted among cancer patients between May 2021 and January 2022. The factors associated with suicidal ideation were used to construct a multivariate logistic regression model, which was visualized as a predictive nomogram to evaluate the risk of suicidal ideation. Areas under the curve, calibration plot, decision curve analysis, and internal and external validation were used to validate the discrimination, calibration and clinical usefulness of the model. RESULTS A total of 820 patients with cancer were recruited for this study and 213 (25.98%) developed suicidal ideation. Levels of demoralization, depression and cancer staging, marital status, residence, medical financial burden, and living condition were influence factors for suicidal ideation. Comparing nomogram with Self-rating Idea of Suicide Scale (SIOSS), the nomogram had a satisfactory discrimination ability with an AUC of 0.859 (95% CI: 0.827-0.890) and 0.818 (95% CI: 0.764-0.873) in the training and validation sets, respectively. The calibration plot and decision curve analysis revealed that this nomogram was in good fitness and could be beneficial in clinical applications. CONCLUSIONS Suicidal ideation is common in cancer patients. Levels of demoralization, depression and cancer staging were independent predictors of suicidal ideation. The nomogram is an effective and simple tool for predictive suicidal ideation in cancer patients.
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Affiliation(s)
- Yuanyuan Luo
- grid.284723.80000 0000 8877 7471School of Nursing, Southern Medical University, No. 1023 Sha Tai South road, Baiyun district, Guangzhou, 510515 China
| | - Qianlin Lai
- grid.284723.80000 0000 8877 7471School of Nursing, Southern Medical University, No. 1023 Sha Tai South road, Baiyun district, Guangzhou, 510515 China
| | - Hong Huang
- grid.284723.80000 0000 8877 7471School of Nursing, Southern Medical University, No. 1023 Sha Tai South road, Baiyun district, Guangzhou, 510515 China
| | - Jiahui Luo
- grid.284723.80000 0000 8877 7471School of Nursing, Southern Medical University, No. 1023 Sha Tai South road, Baiyun district, Guangzhou, 510515 China
| | - Jingxia Miao
- grid.416466.70000 0004 1757 959XDepartment of Medical Oncology, Nanfang Hospital, Southern Medical University, No. 1838, North Guangzhou Avenue, Baiyun District, Guangzhou, 510515 China
| | - Rongrong Liao
- grid.284723.80000 0000 8877 7471First Nursing Unit of Tumor Ward, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, No. 13, Pomegranate Gang Road, Haizhu District, Guangzhou, 510315 China
| | - Zhihui Yang
- School of Nursing, Southern Medical University, No. 1023 Sha Tai South road, Baiyun district, Guangzhou, 510515, China.
| | - Lili Zhang
- School of Nursing, Southern Medical University, No. 1023 Sha Tai South road, Baiyun district, Guangzhou, 510515, China.
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