Skip to main content
SpringerLink
Log in

Prediction of Suicide Risk Using Machine Learning and Big Data

  • Chapter
  • First Online:
Digital Mental Health

Abstract

Suicide remains a preventable, yet persistent public health challenge and is the second leading cause of death among young people. To date, the assessment of suicide risk remains largely subjective, highlighting the urgent need for more objective and precise tools to predict suicide risk at an individual level. Considering this, big data and machine learning models provide an opportunity to facilitate the early implementation of preventive and treatment strategies personalized to each patient. Therefore, in the present chapter we discuss the potential of these tools and describe recent studies using machine learning models to evaluate individualized suicide risk. Furthermore, key considerations, challenges, and the potential ethical implications of the clinical implementation of these algorithms are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 84.99
Price excludes VAT (USA)
Softcover Book
USD 109.99
Price excludes VAT (USA)
Hardcover Book
USD 159.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. World Health Organization (2019) Suicide in the World—Global Health Estimates. WHO

    Google Scholar 

  2. Curtin SC, Warner M, Hedegaard H (2016) Increase in Suicide in the United States, 1999-2014. NCHS Data Brief 1–8.

    Google Scholar 

  3. Katz C, Bolton J, Sareen J. The prevalence rates of suicide are likely underestimated worldwide: why it matters. Soc Psychiatry Psychiatr Epidemiol. 2016;51:125–7.

    Article  Google Scholar 

  4. Bakst SS, Braun T, Zucker I, Amitai Z, Shohat T. The accuracy of suicide statistics: are true suicide deaths misclassified? Soc Psychiatry Psychiatr Epidemiol. 2016;51:115–23.

    Article  Google Scholar 

  5. World Health Organization (2014) Preventing Suicide: A Global Imperative.

    Google Scholar 

  6. Borges G, Nock MK, Haro Abad JM, et al. Twelve-month prevalence of and risk factors for suicide attempts in the World Health Organization World Mental Health Surveys. J Clin Psychiatry. 2010;71:1617–28.

    Article  Google Scholar 

  7. Nock MK, Borges G, Bromet EJ, et al. Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. Br J Psychiatry. 2008;192:98–105.

    Article  Google Scholar 

  8. Arsenault-Lapierre G, Kim C, Turecki G. Psychiatric diagnoses in 3275 suicides: a meta-analysis. BMC Psychiatry. 2004;4:37.

    Article  Google Scholar 

  9. Turecki G, Brent DA. Suicide and suicidal behaviour. Lancet. 2016;387:1227–39.

    Article  Google Scholar 

  10. Barrigon ML, Courtet P, Oquendo M, Baca-García E. Precision medicine and suicide: an opportunity for digital health. Curr Psychiatry Rep. 2019;21:131.

    Article  Google Scholar 

  11. Vancampfort D, Hallgren M, Firth J, Rosenbaum S, Schuch FB, Mugisha J, Probst M, Van Damme T, Carvalho AF, Stubbs B. Physical activity and suicidal ideation: a systematic review and meta-analysis. J Affect Disord. 2018;225:438–48.

    Article  Google Scholar 

  12. Duarte D, El-Hagrassy MM, Couto TCE, Gurgel W, Fregni F, Correa H. Male and female physician suicidality: a systematic review and meta-analysis. JAMA Psychiat. 2020;77:587–97.

    Article  Google Scholar 

  13. Favril L, Yu R, Hawton K, Fazel S. Risk factors for self-harm in prison: a systematic review and meta-analysis. Lancet Psychiatry. 2020;7:682–91.

    Article  Google Scholar 

  14. Broerman R. Diathesis-stress model. In: Zeigler-Hill V, Shackelford TK, editors. Encyclopedia of personality and individual differences. Cham: Springer International Publishing; 2017. p. 1–3.

    Google Scholar 

  15. Machado DB, Rasella D, Dos Santos DN. Impact of income inequality and other social determinants on suicide rate in Brazil. PLoS One. 2015;10:e0124934.

    Article  Google Scholar 

  16. de Bruin A, Agyemang A, Chowdhury MIH. New insights on suicide: uncertainty and political conditions. Appl Econ Lett. 2020;27:1424–9.

    Article  Google Scholar 

  17. Reger MA, Stanley IH, Joiner TE. Suicide mortality and coronavirus disease 2019—a perfect storm? JAMA Psychiat. 2020;77:1093.

    Article  Google Scholar 

  18. John A, Pirkis J, Gunnell D, Appleby L, Morrissey J. Trends in suicide during the covid-19 pandemic. BMJ. 2020;371:m4352.

    Article  Google Scholar 

  19. Gunnell D, Appleby L, Arensman E, et al. Suicide risk and prevention during the COVID-19 pandemic. Lancet Psychiatry. 2020;7:468–71.

    Article  Google Scholar 

  20. Czeisler MÉ, Lane RI, Petrosky E, et al. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic—United States, June 24–30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69:1049–57.

    Article  CAS  Google Scholar 

  21. Fountoulakis KN, Apostolidou MK, Atsiova MB, et al. Self-reported changes in anxiety, depression and suicidality during the COVID-19 lockdown in Greece. J Affect Disord. 2021;279:624–9.

    Article  CAS  Google Scholar 

  22. Fernandes N. Economic effects of coronavirus outbreak (COVID-19) on the world economy. IESE Business School Working Paper No. WP-1240-E. 2020. https://doi.org/10.2139/ssrn.3557504.

  23. Shah R, Eynan R, Heisel MJ, Eden D, Jhirad R, Links PS. Confidential survey into southwestern Ontario suicide: implication for primary care practice. Prim Care Companion CNS Disord. 2018;20(2):17m02217. https://doi.org/10.4088/PCC.17m02217.

    Article  Google Scholar 

  24. Greenhalgh T, Howick J, Maskrey N, Evidence Based Medicine Renaissance Group. Evidence based medicine: a movement in crisis? BMJ. 2014;348:g3725.

    Article  Google Scholar 

  25. Fernandes BS, Williams LM, Steiner J, Leboyer M, Carvalho AF, Berk M. The new field of “precision psychiatry”. BMC Med. 2017;15:80.

    Article  Google Scholar 

  26. Ozomaro U, Wahlestedt C, Nemeroff CB. Personalized medicine in psychiatry: problems and promises. BMC Med. 2013;11:132.

    Article  Google Scholar 

  27. Vieta E. Personalised medicine applied to mental health: precision psychiatry. Rev Psiquiatr Salud Ment. 2015;8:117–8.

    Article  Google Scholar 

  28. Passos IC, Ballester PL, Barros RC, et al. Machine learning and big data analytics in bipolar disorder: a position paper from the International Society for Bipolar Disorders big Data Task Force. Bipolar Disord. 2019;21:582–94.

    Article  Google Scholar 

  29. Passos IC, Mwangi B, Kapczinski F. Big data analytics and machine learning: 2015 and beyond. Lancet Psychiatry. 2016;3:13–5.

    Article  Google Scholar 

  30. Kitchin R, McArdle G. What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data Soc. 2016;3:2053951716631130.

    Article  Google Scholar 

  31. Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol. 2018;14:91–118.

    Article  Google Scholar 

  32. Mitchell TM. Machine Learning. 1st ed. New York: McGraw-Hill Science/Engineering/Math; 1997.

    Google Scholar 

  33. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–60.

    Article  CAS  Google Scholar 

  34. Fazel S, O’Reilly L. Machine learning for suicide research-can it improve risk factor identification? JAMA Psychiat. 2020;77:13–4.

    Article  Google Scholar 

  35. Ahmedani BK, Simon GE, Stewart C, et al. Health care contacts in the year before suicide death. J Gen Intern Med. 2014;29:870–7.

    Article  Google Scholar 

  36. Walby FA, Myhre MØ, Kildahl AT. Contact with mental health services prior to suicide: a systematic review and meta-analysis. Psychiatr Serv. 2018;69:751–9.

    Article  Google Scholar 

  37. Burke TA, Ammerman BA, Jacobucci R. The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: a systematic review. J Affect Disord. 2019;245:869–84.

    Article  Google Scholar 

  38. Just MA, Pan L, Cherkassky VL, McMakin DL, Cha C, Nock MK, Brent D. Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat Hum Behav. 2017;1:911–9.

    Article  Google Scholar 

  39. de Ávila BG, Rabelo-da-Ponte FD, Librenza-Garcia D, Boeira MV, Kauer-Sant’Anna M, Passos IC, Kapczinski F. Potential use of text classification tools as signatures of suicidal behavior: a proof-of-concept study using Virginia Woolf’s personal writings. PLoS One. 2018;13:e0204820.

    Article  Google Scholar 

  40. Kessler RC, Warner CH, Ivany C, et al. Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army study to assess risk and rEsilience in servicemembers (Army STARRS). JAMA Psychiat. 2015;72:49–57.

    Article  Google Scholar 

  41. Bernecker SL, Zuromski KL, Gutierrez PM, et al. Predicting suicide attempts among soldiers who deny suicidal ideation in the Army study to assess risk and resilience in servicemembers (Army STARRS). Behav Res Ther. 2019;120:103350.

    Article  Google Scholar 

  42. Passos IC, Mwangi B, Cao B, et al. Identifying a clinical signature of suicidality among patients with mood disorders: a pilot study using a machine learning approach. J Affect Disord. 2016;193:109–16.

    Article  Google Scholar 

  43. Niculescu AB, Levey DF, Phalen PL, et al. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Mol Psychiatry. 2015;20:1266–85.

    Article  CAS  Google Scholar 

  44. Levey DF, Niculescu EM, Le-Niculescu H, et al. Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment. Mol Psychiatry. 2016;21:768–85.

    Article  CAS  Google Scholar 

  45. Machado CDS, Ballester PL, Cao B, Mwangi B, Caldieraro MA, Kapczinski F, Passos IC. Prediction of suicide attempts in a prospective cohort study with a nationally representative sample of the US population. Psychol Med. 2021:1–12.

    Google Scholar 

  46. Marcon G, Massaro Carneiro Monteiro G, Ballester P, Cassidy RM, Zimerman A, Brunoni AR, von Diemen L, Hauck S, Passos IC. Who attempts suicide among medical students? Acta Psychiatr Scand. 2019;141(3):254–64. https://doi.org/10.1111/acps.13137.

    Article  Google Scholar 

  47. Simon GE, Johnson E, Lawrence JM, et al. Predicting suicide attempts and suicide deaths following outpatient visits using electronic health records. Am J Psychiatry. 2018;175:951–60.

    Article  Google Scholar 

  48. Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci. 2017;5:457–69.

    Article  Google Scholar 

  49. Tsui FR, Shi L, Ruiz V, Ryan ND, Biernesser C, Iyengar S, Walsh CG, Brent DA. Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts. JAMIA Open. 2021;4(1):ooab011. https://doi.org/10.1093/jamiaopen/ooab011.

    Article  Google Scholar 

  50. Cho S-E, Geem ZW, Na K-S. Prediction of suicide among 372,813 individuals under medical check-up. J Psychiatr Res. 2020;131:9–14.

    Article  Google Scholar 

  51. Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA. A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med. 2020;3:78.

    Article  Google Scholar 

  52. Ophir Y, Tikochinski R, Asterhan CSC, Sisso I, Reichart R. Deep neural networks detect suicide risk from textual facebook posts. Sci Rep. 2020;10:16685.

    Article  CAS  Google Scholar 

  53. Liu X, Liu X, Sun J, Yu NX, Sun B, Li Q, Zhu T. Proactive suicide prevention online (PSPO): machine identification and crisis Management for Chinese Social Media Users with Suicidal Thoughts and Behaviors. J Med Internet Res. 2019;21:e11705.

    Article  Google Scholar 

  54. Cummins N, Baird A, Schuller BW. Speech analysis for health: current state-of-the-art and the increasing impact of deep learning. Methods. 2018;151:41–54.

    Article  CAS  Google Scholar 

  55. Pastina JP, Sorter M, Connolly B, Bretonnel Cohen K, McCullumsmith C, Gee JT, Morency L-P, Scherer S, Rohlfs L, STM Research Group. A machine learning approach to identifying the thought markers of suicidal subjects: a prospective multicenter trial. Suicide Life Threat Behav. 2017;47:112–21.

    Article  Google Scholar 

  56. Cohen J, Wright-Berryman J, Rohlfs L, Wright D, Campbell M, Gingrich D, Santel D, Pestian J. A feasibility study using a machine learning suicide risk prediction model based on open-ended interview language in adolescent therapy sessions. Int J Environ Res Public Health. 2020;17(21):8187. https://doi.org/10.3390/ijerph17218187.

    Article  Google Scholar 

  57. Dev S, Kim D. State- and county-level social capital as predictors of county-level suicide Rates in the United States: A Lagged Multilevel Study. Public Health Rep. 2021;136(5):538–42.

    Article  Google Scholar 

  58. Linthicum KP, Schafer KM, Ribeiro JD. Machine learning in suicide science: applications and ethics. Behav Sci Law. 2019;37:214–22.

    Article  Google Scholar 

  59. Hariman K, Ventriglio A, Bhugra D. The future of digital psychiatry. Curr Psychiatry Rep. 2019;21:88.

    Article  Google Scholar 

  60. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S-I. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2:56–67.

    Article  Google Scholar 

  61. Belsher BE, Smolenski DJ, Pruitt LD, Bush NE, Beech EH, Workman DE, Morgan RL, Evatt DP, Tucker J, Skopp NA. Prediction models for suicide attempts and deaths: a systematic review and simulation. JAMA Psychiat. 2019;76:642–51.

    Article  Google Scholar 

  62. Simon GE, Shortreed SM, Coley RY. Positive predictive values and potential success of suicide prediction models. JAMA Psychiat. 2019;76:868–9.

    Article  Google Scholar 

  63. O’Neil C Weapons of math destruction: how big data increases inequality and threatens democracy. Crown Publishing Group; 2016.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil

    Thiago Henrique Roza, Thyago Antonelli Salgado, Cristiane Santos Machado, Júlio Bebber & Thales Freitas

  2. Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil

    Thiago Henrique Roza, Thyago Antonelli Salgado & Cristiane Santos Machado

  3. Department of Psychiatry, Universidade Federal do Rio Grande do Sul, School of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Porto Alegre, RS, Brazil

    Thiago Henrique Roza, Thyago Antonelli Salgado & Cristiane Santos Machado

  4. Neuroscience Graduate Program, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

    Devon Watts

  5. Department of Social, Genetic and Developmental Psychiatry, King’s College of London, London, UK

    Francisco Diego Rabelo-da-Ponte

  6. Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada

    Flavio Kapczinski

  7. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Ives Cavalcante Passos

Authors
  1. Thiago Henrique Roza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thyago Antonelli Salgado
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cristiane Santos Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Devon Watts
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Júlio Bebber
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thales Freitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Francisco Diego Rabelo-da-Ponte
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Flavio Kapczinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ives Cavalcante Passos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Ives Cavalcante Passos

  2. Department of Social, Genetic and Developmental Psychiatry, King’s College of London, London, UK

    Francisco Diego Rabelo-da-Ponte

  3. Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada

    Flavio Kapczinski

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Roza, T.H. et al. (2023). Prediction of Suicide Risk Using Machine Learning and Big Data. In: Passos, I.C., Rabelo-da-Ponte, F.D., Kapczinski, F. (eds) Digital Mental Health. Springer, Cham. https://doi.org/10.1007/978-3-031-10698-9_11

Download citation

Publish with us

Policies and ethics

Search

Navigation