A machine learning approach for predicting suicidal thoughts and behaviours among college students

Macalli, Mélissa; Navarro, Marie C.; Orri, Massimiliano; Tournier, Marie; Thiébaut, Rodolphe; Côté, Sylvana M.; Tzourio, Christophe

doi:10.1038/s41598-021-90728-z

Cited by 36 publications

(28 citation statements)

References 48 publications

(49 reference statements)

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“…The baseline PHQ-8 score was the most prominent independent predictor of SI, accounting for 12% of the variance. This reflects a consistent finding in the literature that depression severity is highly associated with SI [ 60 ] and with suicide-related outcomes [ 61 ] and illustrates the importance of antidepressant treatment.…”

Section: Discussionsupporting

confidence: 88%

Telehealth-Supported Decision-making Psychiatric Care for Suicidal Ideation: Longitudinal Observational Study

O’Callaghan¹,

Mahrer

Belanger

et al. 2022

JMIR Form Res

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Background Suicide is a leading cause of death in the United States, and suicidal ideation (SI) is a significant precursor and risk factor for suicide. Objective This study aimed to examine the impact of a telepsychiatric care platform on changes in SI over time and remission, as well as to investigate the relationship between various demographic and medical factors on SI and SI remission. Methods Participants included 8581 US-based adults (8366 in the treatment group and 215 in the control group) seeking treatment for depression, anxiety, or both. The treatment group included patients who had completed at least 12 weeks of treatment and had received a prescription for at least one psychiatric medication during the study period. Providers prescribed psychiatric medications for each patient during their first session and received regular data on participants. They also received decision support at treatment onset via the digital platform, which leveraged an empirically derived proprietary precision-prescribing algorithm to give providers real-time care guidelines. Participants in the control group consisted of individuals who completed the initial enrollment data and completed surveys at baseline and 12 weeks but did not receive care. Results Greater feelings of hopelessness, anhedonia, and feeling bad about oneself were most significantly correlated (r=0.24-0.37) with SI at baseline. Sleep issues and feeling tired or having low energy, although significant, had lower correlations with SI (r=0.13-0.14). In terms of demographic variables, advancing age and education were associated with less SI at baseline (r=−0.16) and 12 weeks (r=−0.10) but less improvement over time (r=−0.12 and −0.11, respectively). Although not different at baseline, the SI expression was evident in 34.4% (74/215) of the participants in the control group and 12.32% (1031/8366) of the participants in the treatment group at 12 weeks. Although the participants in the treatment group improved over time regardless of various demographic variables, participants in the control group with less education worsened over time, after controlling for age and depression severity. A model incorporating the treatment group, age, sex, and 8-item Patient Health Questionnaire scores was 77% accurate in its classification of complete remission. Those in the treatment group were 4.3 times more likely (odds ratio 4.31, 95% CI 2.88-6.44) to have complete SI remission than those in the control group. Female participants and those with advanced education beyond high school were approximately 1.4 times more likely (odds ratio 1.38, 95% CI 1.18-1.62) to remit than their counterparts. Conclusions The results highlight the efficacy of an antidepressant intervention in reducing SI, in this case administered via a telehealth platform and with decision support, as well as the importance of considering covariates, or subpopulations, when considering SI. Further research and refinement, ideally via randomized controlled trials, are needed.

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Section: Discussionsupporting

confidence: 88%

Telehealth-Supported Decision-making Psychiatric Care for Suicidal Ideation: Longitudinal Observational Study

O’Callaghan¹,

Mahrer

Belanger

et al. 2022

JMIR Form Res

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“…Generally, machine learning has been widely used in a series of mental health risk predictions about college students' depression, stress[ 85 ] and suicidal behavior[ 86 , 87 ]. Big data brings many benefits to the prediction of psychological states by reducing the subjectivity of human judgment or human operations to a certain extent and relieving the concerns of patients about possible social stigma and discrimination.…”

Section: Predicting Depressionmentioning

confidence: 99%

Influencing factors, prediction and prevention of depression in college students: A literature review

Liu¹,

Guo²,

Zhang³

et al. 2022

WJP

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The high prevalence of depression among college students has a strong negative impact on individual physical and mental health, academic development, and interpersonal communication. This paper reviewed the extant literature by identifying nonpathological factors related to college students' depression, investigating the methods of predicting depression, and exploring nonpharmaceutical interventions for college students' depression. The influencing factors of college students' depression mainly fell into four categories: biological factors, personality and psychological state, college experience, and lifestyle. The outbreak of coronavirus disease 2019 has exacerbated the severity of depression among college students worldwide and poses grave challenges to the prevention and treatment of depression, given that the coronavirus has spread quickly with high infection rates, and the pandemic has changed the daily routines of college life. To predict and measure mental health, more advanced methods, such as machine algorithms and artificial intelligence, have emerged in recent years apart from the traditional commonly used psychological scales. Regarding nonpharmaceutical prevention measures, both general measures and professional measures for the prevention and treatment of college students' depression were examined in this study. Students who experience depressive disorders need family support and personalized interventions at college, which should also be supplemented by professional interventions such as cognitive behavioral therapy and online therapy. Through this literature review, we insist that the technology of identification, prediction, and prevention of depression among college students based on big data platforms will be extensively used in the future. Higher education institutions should understand the potential risk factors related to college students' depression and make more accurate screening and prevention available with the help of advanced technologies.

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“…As found in our study, stressful events can also lead to other negative developmental outcomes [ 21 , 102 – 105 ], including mental illness, a known correlate of attempted suicide among AYA [ 7 – 12 , 118 ]. Self‐concept, related to mental health, is an important psychosocial factor that changes during adolescence and young adulthood.…”

Section: Discussionmentioning

confidence: 62%

HIV and suicide risk across adolescence and young adulthood: an examination of socio‐demographic, contextual and psychosocial risk factors for attempted suicide in a longitudinal cohort of ageing adolescents affected by HIV living in the New York City Area

et al. 2022

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Introduction As children become adolescents and young adults (AYA), their risk for attempting suicide increases dramatically, with chronic health conditions an important risk factor. This study examined correlates of suicidality across development in AYA living with perinatally acquired HIV (AYALPHIV) and those perinatally HIV‐exposed but uninfected (AYAPHEU). Methods Data come from an ongoing longitudinal New York City‐based study (N = 339) with AYALPHIV and AYAPHEU interviewed every 12–18 months from 2003 to 2019 (mean enrolment age = 12 years; current mean age = 27 years). The Diagnostic Interview Schedule for Children (adolescent or young adult version) assessed psychiatric disorders and first‐reported suicide attempt. Generalized estimating equations were used to examine associations between first‐reported suicide attempt and socio‐demographic, contextual and psychosocial correlates measured concurrently across six timepoints. Results At enrolment, 51% of participants were female, 72% heterosexual, 60% Black and 50% Latinx. Attempted suicide was significantly higher among AYALPHIV (27%, CI 21–33%) compared to AYAPHEU (16%, CI 10–22%), with an OR of 1.74 (CI 1.04–2.92) in a model adjusting for age. For AYALPHIV, anxiety (OR 2.66, CI 1.46–4.85), mood (OR 3.62, CI 1.49–8.81) and behaviour disorders (OR 5.05, CI 2.15–11.87) and past‐year arrest (OR 3.05, CI 1.26–7.4), negative life events (OR 1.27, CI 1.11–1.46), city stress (OR 2.28, CI 1.46–3.57), pregnancy (OR 2.28, CI 1.08–4.81) and HIV stigma (OR 2.46, CI 1.27–4.75) were associated with increased odds of attempted suicide, while identifying as heterosexual (OR 0.27, CI 0.14–0.52), higher personal (OR 0.45, CI 0.26–0.80) and family self‐concept (OR 0.36, CI 0.22–0.57) were protective. Interactions by HIV status and age were found: substance use was more strongly associated with attempted suicide among AYAPHEU than AYALPHIV, while negative life events and higher religiosity were more strongly associated with increased odds of attempted suicide among AYA ≥ 19 versus ≤ 18 years. Conclusions AYALPHIV compared to AYAPHEU faced unique risks for attempted suicide as they age into adulthood, with the highest risk among AYALPHIV experiencing HIV stigma or pregnancy and the highest risk among AYAPHEU with substance use. Assessing for suicide risk and correlates with attention to ageing can inform preventive interventions tailored to meet AYALPHIV and AYAPHEU needs.

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A machine learning approach for predicting suicidal thoughts and behaviours among college students

Cited by 36 publications

References 48 publications

Telehealth-Supported Decision-making Psychiatric Care for Suicidal Ideation: Longitudinal Observational Study

Telehealth-Supported Decision-making Psychiatric Care for Suicidal Ideation: Longitudinal Observational Study

Influencing factors, prediction and prevention of depression in college students: A literature review

HIV and suicide risk across adolescence and young adulthood: an examination of socio‐demographic, contextual and psychosocial risk factors for attempted suicide in a longitudinal cohort of ageing adolescents affected by HIV living in the New York City Area

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