Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population
2
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.
Abstract
Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.
참고문헌 (34)
- Annual Report on the Cause of Death Statistics 2016, "Statistics Korea", Statistics Korea, ~ (2017)
- "OECD Health Statistics 2017" (2017)[URL]
- Kim WJ, "Internalized stigma and its psychosocial correlates in Korean patients with serious mental illness", Psychiatry Res, 225, pp.433~439 (2015)
- Arsenault-Lapierre G, "Psychiatric diagnoses in 3275suicides: a meta-analysis", BMC Psychiatry, 4, pp.37~ (2004)
- Conwell Y, "Relationships of age and axis I diagnoses in victims of completed suicide: a psychological autopsy study", Am J Psychiatry, 153, pp.1001~1008 (1996)
- "Psychological Autopsy Report" (2018)[URL]
- Zalsman G, "Suicide prevention strategies revisited: 10-year systematic review", Lancet Psychiatry, 3, pp.646~659 (2016)
- The Survey of Mental Disorders in Korea 2016, "Ministry of Health", Ministry of Health and Welfare, ~ (2017)
- Nock MK, "Cross-national prevalence and risk factors for suicidal ideation, plans and attempts", Br J Psychiatry, 192, pp.98~105 (2008)
- May AM, "Predicting future suicide attempts among depressed suicide ideators: a 10-year longitudinal study", J Psychiatr Res, 46, pp.946~952 (2012)
- Miranda R, "Characteristics of suicidal ideation that predict the transition to future suicide attempts in adolescents", J Child Psychol Psychiatry, 55, pp.1288~1296 (2014)
- Chellappa SL, "Sleep disorders and suicidal ideation in patients with depressive disorder", Psychiatry Res, 153, pp.131~136 (2007)
- Valtonen H, "Suicidal ideation and attempts in bipolar I and II disorders", J Clin Psychiatry, 66, pp.1456~1462 (2005)
- Jeon HJ, "Lifetime prevalence and correlates of suicidal ideation, plan, and single and multiple attempts in a Korean nationwide study", J Nerv Ment Dis, 198, pp.643~646 (2010)
- Ribeiro JD, "Letter to the Editor: suicide as a complex classification problem:machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016)", Psychol Med, 46, pp.2009~2010 (2016)
- Obermeyer Z, "Predicting the future - big data, machine learning, and clinical medicine", N Engl J Med, 375, pp.1216~1219 (2016)
- Kweon S, "Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES).", Int J Epidemiol, 43, pp.69~77 (2014)
- Li DC, "A learning method for the class imbalance problem with medical data sets", Comput Biol Med, 40, pp.509~518 (2010)
- Bolton JM, "Suicide risk assessment and intervention in people with mental illness", BMJ, 351 (2015)
- Ghasemi P, "Measurement scales of suicidal ideation and attitudes: a systematic review article", Health Promot Perspect, 5, pp.156~168 (2015)
- Passos IC, "Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach", J Affect Disord, 193, pp.109~116 (2016)
- Oh J, "Classification of suicide attempts through a machine learning algorithm based on multiple systemic psychiatric scales.", Front Psychiatry, 8, pp.192~ (2017)
- Choi SB, "Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea", J Affect Disord, 231, pp.8~14 (2018)
- Altman N, "The curse(s) of dimensionality", Nat Methods, 15, pp.399~400 (2018)
- Grover KE, "Problem solving moderates the effects of life event stress and chronic stress on suicidal behaviors in adolescence", J Clin Psychol, 65, pp.1281~1290 (2009)
- Tsutsumi A, "Jichi Medical School Cohort Study Group. Low control at work and the risk of suicide in Japanese men: a prospective cohort study", Psychother Psychosom, 76, pp.177~185 (2007)
- Druss B, "Suicidal ideation and suicide attempts in general medical illnesses", Arch Intern Med, 160, pp.1522~1526 (2000)
- 전홍진, "Gender Differences in Somatic Symptoms and Current Suicidal Risk in Outpatients with Major Depressive Disorder", PSYCHIATRY INVESTIGATION, 13(6), pp.609~615 (2016), 대한신경정신의학회[DOI]
- Ahmedani BK, "Major Physical Health Conditions and Risk of Suicide", Am J Prev Med, 53, pp.308~315 (2017)
- Lee H, "Age and sex-related differences in risk factors for elderly suicide: differentiating between suicide ideation and attempts", Int J Geriatr Psychiatry, 33, pp.e300~e306 (2018)
- Sugawara N, "Gender differences in factors associated with suicidal ideation and depressive symptoms among middle-aged workers in Japan", Ind Health, 51, pp.202~213 (2013)
- Kimura T, "Educational Levels and Risk of Suicide in Japan: The Japan Public Health Center Study (JPHC) Cohort I", J Epidemiol, 26, pp.315~321 (2016)
- 김선영, "Gender Differences of Occupational Stress Associated with Suicidal Ideation among South Korean Employees: The Kangbuk Samsung Health Study", PSYCHIATRY INVESTIGATION, 15(2), pp.156~163 (2018), 대한신경정신의학회[DOI]
- Yoon CG, "Is suicidal ideation linked to working hours and shift work in Korea?", J Occup Health, 57, pp.222~229 (2015)