Suicide Risk Assessment and Prevention: A Literature Review

  • Authors

    • Nagavali Saka
    • S. Murali Krishna
    https://doi.org/10.14419/ijet.v7i4.16.28819
  • Suicide, Risk, Assessment, Prevention, Review, Data Mining.
  • Suicide is the act of taking own life purposefully. Suicide trail aims at harming oneself with mortal intention. The behavior of the people committing suicide can be analyzed over a range of activities like thinking, setting up and committing suicide. Global study says that the second major cause for deaths today is suicide. Nearly, 71% of the deaths in women and 50% of the deaths in men these days are because of suicides. In some countries, the highest suicide rate found among the people aged above 70 years and in some countries it is more among the teenagers aged between 15 and 29.Suicidal behavior indicates deep unhappiness and sometimes mental disorder. Both factors are not necessarily dependent on each other. The objective of this study is to present the literature review on suicidal risk assessment and prevention mechanisms proposed by different researchers using data mining and machine learning techniques to minimize the suicidal rate.

     

     


     
  • References

    1. [1] Alonso, S. G., De La Torre-Díez, I., Hamrioui, S., López-Coronado, M., Barreno, D. C., Nozaleda, L. M., & Franco, M. (2018). Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. Journal of medical systems, 42(9), 161

      [2] Bachmann, M., Päeske, L., Kalev, K., Aarma, K., Lehtmets, A., Ööpik, P., ... &Hinrikus, H. (2018). Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer methods and programs in biomedicine, 155, 11-17.

      [3] Hollingsworth, D. W., Slish, M. L., Wingate, L. R., Davidson, C. L., Rasmussen, K. A., O'Keefe, V. M., ... & Grant, D. M. (2018). The indirect effect of perceived burdensomeness on the relationship between indices of social support and suicide ideation in college students. Journal of American college health, 66(1), 9-16.

      [4] Ivanich, J., & Teasdale, B. (2018). Suicide Ideation among Adolescent American Indians: An Application of General Strain Theory. Deviant Behavior, 39(6), 702-715.

      [5] Nock, M. K., Millner, A. J., Joiner, T. E., Gutierrez, P. M., Han, G., Hwang, I., ... & Stein, M. B. (2018). Risk factors for the transition from suicide ideation to suicide attempt: Results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Journal of abnormal psychology, 127(2), 139

      [6] Ruderfer, D. M., Walsh, C. G., Aguirre, M. W., Ribeiro, J. D., Franklin, J. C., & Rivas, M. A. (2018). Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide. bioRxiv, 266411.

      [7] Anastasiades, M. H., Kapoor, S., Wootten, J., &Lamis, D. A. (2017). Perceived stress, depressive symptoms, and suicidal ideation in undergraduate women with varying levels of mindfulness. Archives of women's mental health, 20(1), 129-138.

      [8] Bisson, K. H. (2017). The Effect of Anxiety and Depression on College Students’ Academic Performance: Exploring Social Support as a Moderator.

      [9] Choi, S. B., Lee, W., Yoon, J. H., Won, J. U., & Kim, D. W. (2017). Risk factors of suicide attempt among people with suicidal ideation in South Korea: a cross-sectional study. BMC public health, 17(1), 579.

      [10] De Crescenzo, F., Serra, G., Maisto, F., Uchida, M., Woodworth, H., Casini, M. P., ... &Vicari, S. (2017). Suicide Attempts in Juvenile Bipolar Versus Major Depressive Disorders: Systematic Review and Meta-Analysis. Journal of the American Academy of Child & Adolescent Psychiatry.

      [11] Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., ... &Kleiman, E. M., Turner, B. J., Fedor, S., Beale, E. E., Huffman, J. C., & Nock, M. K. (2017) Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies. Journal of abnormal psychology, 126(6), 726.

      [12] Nock, M. K. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin, 143(2), 187.

      [13] Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 5(3), 457-469.

      [14] Amini, P., Ahmadinia, H., Poorolajal, J., &Amiri, M. M. (2016). Evaluating the high risk groups for suicide: A comparison of logistic regression, support vector machine, decision tree and artificial neural network. Iranian journal of public health, 45(9), 1179

      [15] Berlin, Heidelberg.T. (2016). Predicting suicide following self-harm: systematic review of risk factors and risk scales. The British Journal of Psychiatry, 209(4), 277-283

      [16] Cook, B. L., Progovac, A. M., Chen, P., Mullin, B., Hou, S., & Baca-Garcia, E. (2016). Novel use of natural language processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid. Computational and mathematical methods in medicine, 2016

      [17] Kõlves, K., & De Leo, D. (2016). Adolescent suicide rates between 1990 and 2009: Analysis of age group 15–19 years worldwide. Journal of Adolescent Health, 58(1), 69-77.

      [18] Ramasubbu, R., Brown, M. R., Cortese, F., Gaxiola, I., Goodyear, B., Greenshaw, A. J., ... & Greiner, R. (2016). Accuracy of automated classification of major depressive disorder as a function of symptom severity. NeuroImage: Clinical, 12, 320-331

      [19] Yu, J. S., Xue, A. Y., Redei, E. E., &Bagheri, N. (2016). A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Translational psychiatry, 6(10), e931

      [20] Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E., Puthankatti, S. D., &Adeli, A. (2015). A novel depression diagnosis index using nonlinear features in EEG signals. European neurology, 74(1-2), 79-83

      [21] Bolton, J. M., Gunnell, D., &Turecki, G. (2015). Suicide risk assessment and intervention in people with mental illness. BMJ: British Medical Journal (Online), 351

      [22] Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., &Quatieri, T. F. (2015). A review of depression and suicide risk assessment using speech analysis. Speech Communication, 71, 10-49

      [23] DeVylder, J. E., Lukens, E. P., Link, B. G., & Lieberman, J. A. (2015). Suicidal ideation and suicide attempts among adults with psychotic experiences: data from the collaborative psychiatric epidemiology surveys. JAMA psychiatry, 72(3), 219-225

      [24] Bernert, R. A., Hom, M. A., & Roberts, L. W. (2014). A review of multidisciplinary clinical practice guidelines in suicide prevention: toward an emerging standard in suicide risk assessment and management, training and practice. Academic Psychiatry, 38(5), 585-592

      [25] Chakraborty, R., Chatterjee, A., &Chaudhury, S. (2014). Impact of substance use disorder on presentation and short-term course of schizophrenia. Psychiatry journal, 2014

      [26] Shaheen, H., Jahan, M., &Shaheen, S. (2014). A Study of Loneliness in Relation to Well-Being Among Adolescents. Self, 14, 2-836.

      [27] Tucker, R. P., O’Keefe, V. M., Cole, A. B., Rhoades-Kerswill, S., Hollingsworth, D. W., Helle, A. C.,& Wingate, L. R. (2014). Mindfulness tempers the impact of personality on suicidal ideation. Personality and individual differences, 68, 229-233.

      [28] Zhang, Y., Cui, H., Burkell, J., & Mercer, R. E. (2014). A machine learning approach for rating the quality of depression treatment web pages. iConference 2014 Proceedings.

      [29] Khodayari-Rostamabad, A., Reilly, J. P., Hasey, G. M., de Bruin, H., &MacCrimmon, D. J. (2013). A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clinical Neurophysiology, 124(10), 1975-1985.

      [30] Hosseinifard, B., Moradi, M. H., &Rostami, R. (2013). Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Computer methods and programs in biomedicine, 109(3), 339-345.

      [31] Zhu, D., Li, X., Jiang, X., Chen, H., Shen, D., & Liu, T. (2013, June). Exploring high-order functional interactions via structurally-weighted lasso models. In International Conference on Information Processing in Medical Imaging (pp. 13-24). Springer,

  • Downloads

  • How to Cite

    Saka, N., & Murali Krishna, S. (2018). Suicide Risk Assessment and Prevention: A Literature Review. International Journal of Engineering & Technology, 7(4.16), 318-320. https://doi.org/10.14419/ijet.v7i4.16.28819