COVID-19 and Suicide Tendency: Prediction and Risk Factor Analysis Using Machine Learning and Explainable AI

Authors

DOI:

https://doi.org/10.4108/eetpht.10.3070

Keywords:

COVID-19, Suicide, Machine Learning, Risk Factor, Explainable AI

Abstract

INTRODUCTION: Pandemics and epidemics have frequently led to a significant increase in the suicide rate in affected regions. However, these unnecessary deaths can be prevented by identifying the risk factors and intervening earlier with those at risk. Numerous empirical studies have exhaustively documented multiple suicide risk factors. In addition, many evidence-based approaches have employed machine learning models to diagnose vulnerable groups, a task that would otherwise be challenging if only human cognition were employed. To date, to the best of our knowledge, no research has been conducted on COVID-19-related suicide prediction.

OBJECTIVES: This research, aims to develop a machine-learning model capable of identifying individuals who are contemplating suicide due to COVID-19-related complexities and assessing the potential risk factors.

METHODS: We trained a gradient-boosting model based on tree-based learners on 10067 data consisting of 76 features, which were primarily responses to socio-demographic, behavioural, and psychological questions about COVID-19 and suicidal behaviours.

RESULTS: The final model predicted individuals at risk with an auROC score of 0.77 and a 95% confidence interval of 0.77 to 0.88. The optimal cutoff produced a sensitivity of 31.37 percent and a specificity of 82.35 percent in predicting suicidal tendencies. However, the auPRC was only 0.26, with a 95 percent confidence interval of 0.13 to 0.38, as the class distribution was extremely unbalanced. Consequently, the scores for precision and recall were 0.35 and 0.31, respectively.

CONCLUSION: We investigated the risk factors, the majority of which were associated with sleeping difficulties, fear of COVID-19, social interactions, and other socio-demographic factors. The identified risk factors can be considered when formulating a policy to prevent COVID-19-related suicides, which can impose a long-term economic and health burden on society.

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References

Wasserman IM. The impact of epidemic, war, prohibition and media on suicide: United States, 1910–1920. Suicide and Life‐Threatening Behavior. 1992 Jun;22(2):240-54. DOI: https://doi.org/10.1111/j.1943-278X.1992.tb00231.x

Cheung YT, Chau PH, Yip PS. A revisit on older adults suicides and Severe Acute Respiratory Syndrome (SARS) epidemic in Hong Kong. International Journal of Geriatric Psychiatry: A journal of the psychiatry of late life and allied sciences. 2008 Dec;23(12):1231-8.

Cheung YT, Chau PH, Yip PS. A revisit on older adults suicides and Severe Acute Respiratory Syndrome (SARS) epidemic in Hong Kong. International Journal of Geriatric Psychiatry: A journal of the psychiatry of late life and allied sciences. 2008 Dec;23(12):1231-8. DOI: https://doi.org/10.1002/gps.2056

Elovainio M, Hakulinen C, Pulkki-Råback L, Virtanen M, Josefsson K, Jokela M, Vahtera J, Kivimäki M. Contribution of risk factors to excess mortality in isolated and lonely individuals: an analysis of data from the UK Biobank cohort study. The Lancet Public Health. 2017 Jun 1;2(6):e260-6. DOI: https://doi.org/10.1016/S2468-2667(17)30075-0

Matthews T, Danese A, Caspi A, Fisher HL, Goldman-Mellor S, Kepa A, Moffitt TE, Odgers CL, Arseneault L. Lonely young adults in modern Britain: findings from an epidemiological cohort study. Psychological medicine. 2019 Jan;49(2):268-77. DOI: https://doi.org/10.1017/S0033291718000788

O'Connor RC, Kirtley OJ. The integrated motivational–volitional model of suicidal behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences. 2018 Sep 5;373(1754):20170268. DOI: https://doi.org/10.1098/rstb.2017.0268

Yao H, Chen JH, Xu YF. Patients with mental health disorders in the COVID-19 epidemic.

Barr B, Taylor-Robinson D, Scott-Samuel A, McKee M, Stuckler D. Suicides associated with the 2008-10 economic recession in England: time trend analysis. Bmj. 2012 Aug 14;345:e5142.

Barr B, Taylor-Robinson D, Scott-Samuel A, McKee M, Stuckler D. Suicides associated with the 2008-10 economic recession in England: time trend analysis. Bmj. 2012 Aug 14;345:e5142.

Barr B, Taylor-Robinson D, Scott-Samuel A, McKee M, Stuckler D. Suicides associated with the 2008-10 economic recession in England: time trend analysis. Bmj. 2012 Aug 14;345:e5142. DOI: https://doi.org/10.1136/bmj.e5142

Gunnell D, Appleby L, Arensman E, Hawton K, John A, Kapur N, Khan M, O'Connor RC, Pirkis J, Caine ED, Chan LF. Suicide risk and prevention during the COVID-19 pandemic. The Lancet Psychiatry. 2020 Jun 1;7(6):468-71. DOI: https://doi.org/10.1016/S2215-0366(20)30171-1

Garfin DR, Silver RC, Holman EA. The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure. Health psychology. 2020 May;39(5):355. DOI: https://doi.org/10.1037/hea0000875

Torok M, Han J, Baker S, Werner-Seidler A, Wong I, Larsen ME, Christensen H. Suicide prevention using self-guided digital interventions: a systematic review and meta-analysis of randomised controlled trials. The Lancet Digital Health. 2020 Jan 1;2(1):e25-36. DOI: https://doi.org/10.1016/S2589-7500(19)30199-2

Hill RM, Oosterhoff B, Do C. Using machine learning to identify suicide risk: a classification tree approach to prospectively identify adolescent suicide attempters. Archives of suicide research. 2020 Apr 2;24(2):218-35. DOI: https://doi.org/10.1080/13811118.2019.1615018

Berman NC, Stark A, Cooperman A, Wilhelm S, Cohen IG. Effect of patient and therapist factors on suicide risk assessment. Death studies. 2015 Aug 9;39(7):433-41. DOI: https://doi.org/10.1080/07481187.2014.958630

Joiner Jr TE, Walker RL, Rudd MD, Jobes DA. Scientizing and routinizing the assessment of suicidality in outpatient practice. Professional psychology: Research and practice. 1999 Oct;30(5):447. DOI: https://doi.org/10.1037//0735-7028.30.5.447

Bryan CJ, Rudd MD. Advances in the assessment of suicide risk. Journal of clinical psychology. 2006 Feb;62(2):185-200. DOI: https://doi.org/10.1002/jclp.20222

McCarthy JF, Bossarte RM, Katz IR, Thompson C, Kemp J, Hannemann CM, Nielson C, Schoenbaum M. Predictive modeling and concentration of the risk of suicide: implications for preventive interventions in the US Department of Veterans Affairs. American journal of public health. 2015 Sep;105(9):1935-42. DOI: https://doi.org/10.2105/AJPH.2015.302737

Bolton JM, Spiwak R, Sareen J. Predicting suicide attempts with the SAD PERSONS scale: a longitudinal analysis. The Journal of clinical psychiatry. 2012 Jun 15;73(6):15009. DOI: https://doi.org/10.4088/JCP.11m07362

Su C, Aseltine R, Doshi R, Chen K, Rogers SC, Wang F. Machine learning for suicide risk prediction in children and adolescents with electronic health records. Translational psychiatry. 2020 Nov 26;10(1):413. DOI: https://doi.org/10.1038/s41398-020-01100-0

De La Garza ÁG, Blanco C, Olfson M, Wall MM. Identification of suicide attempt risk factors in a national US survey using machine learning. JAMA psychiatry. 2021 Apr 1;78(4):398-406. DOI: https://doi.org/10.1001/jamapsychiatry.2020.4165

Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science. 2017 May;5(3):457-69. DOI: https://doi.org/10.1177/2167702617691560

Mann JJ, Ellis SP, Waternaux CM, Liu X, Oquendo MA, Malone KM, Brodsky BS, Haas GL, Currier D. Classification trees distinguish suicide attempters in major psychiatric disorders: a model of clinical decision making. Journal of Clinical Psychiatry. 2008 Jan 1;69(1):23. DOI: https://doi.org/10.4088/JCP.v69n0104

Kessler RC, Stein MB, Petukhova MV, Bliese P, Bossarte RM, Bromet EJ, Fullerton CS, Gilman SE, Ivany C, Lewandowski-Romps L, Millikan Bell A. Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Molecular psychiatry. 2017 Apr;22(4):544-51.

Pakpour AH, Al Mamun F, Hosen I, Griffiths MD, Mamun MA. A population-based nationwide dataset concerning the COVID-19 pandemic and serious psychological consequences in Bangladesh. Data in brief. 2020 Dec 1;33:106621. DOI: https://doi.org/10.1016/j.dib.2020.106621

James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York: springer; 2013 Jun. DOI: https://doi.org/10.1007/978-1-4614-7138-7

Fernández-Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems?. The journal of machine learning research. 2014 Jan 1;15(1):3133-81.

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems. 2017;30.

Raskutti G, Wainwright MJ, Yu B. Early stopping and non-parametric regression: an optimal data-dependent stopping rule. The Journal of Machine Learning Research. 2014 Jan 1;15(1):335-66.

Jeni LA, Cohn JF, De La Torre F. Facing imbalanced data--recommendations for the use of performance metrics. In2013 Humaine association conference on affective computing and intelligent interaction 2013 Sep 2 (pp. 245-251). IEEE. DOI: https://doi.org/10.1109/ACII.2013.47

Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Advances in neural information processing systems. 2017;30.

Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DK, Newman SF, Kim J, Lee SI. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature biomedical engineering. 2018 Oct;2(10):749-60. DOI: https://doi.org/10.1038/s41551-018-0304-0

Johnson RW. An introduction to the bootstrap. Teaching statistics. 2001 Jun 1;23(2):49-54. DOI: https://doi.org/10.1111/1467-9639.00050

Hasan MM, Knight P, Tania MH, Bitto AK, Das A, Punja H. A Novel Framework for Co-designing of An Artificial Intelligence Based Television-enabled Application to Address Social Isolation and Alleviating Loneliness for Older People. In2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) 2022 Dec 2 (pp. 42-47). IEEE. DOI: https://doi.org/10.1109/SKIMA57145.2022.10029400

Biplob KB, Bijoy MH, Bitto AK, Das A, Chowdhury A, Hossain SM. Suicidal Ratio Prediction Among the Continent of World: A Machine Learning Approach. In2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) 2023 Apr 21 (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICAIA57370.2023.10169618

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Published

18-03-2024

How to Cite

1.
Khalid Been Badruzzaman Biplob, Musabbir Hasan Sammak, Abu Kowshir Bitto, Imran Mahmud. COVID-19 and Suicide Tendency: Prediction and Risk Factor Analysis Using Machine Learning and Explainable AI. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 18 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/3070