{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T07:07:45Z","timestamp":1790320065849,"version":"4.1.0"},"reference-count":48,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["NRF-2019R1F1A1049662"],"award-info":[{"award-number":["NRF-2019R1F1A1049662"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006107","name":"Gil Medical Center, Gachon University","doi-asserted-by":"publisher","award":["FRD2019-02-02"],"award-info":[{"award-number":["FRD2019-02-02"]}],"id":[{"id":"10.13039\/501100006107","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged &gt; 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.<\/jats:p>","DOI":"10.3390\/ijerph181910150","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T10:39:20Z","timestamp":1632739160000},"page":"10150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Development of a Suicide Prediction Model for the Elderly Using Health Screening Data"],"prefix":"10.3390","volume":"18","author":[{"given":"Seo-Eun","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon 21565, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0370-5562","authenticated-orcid":false,"given":"Zong Woo","family":"Geem","sequence":"additional","affiliation":[{"name":"College of IT Convergence, Gachon University, Seongnam 13120, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0148-9827","authenticated-orcid":false,"given":"Kyoung-Sae","family":"Na","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon 21565, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021, July 28). Suicide. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/suicide."},{"key":"ref_2","unstructured":"World Health Organization (2021, July 28). WHO Mental Health Action Plan 2013\u20132030. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789241506021."},{"key":"ref_3","unstructured":"American Foundation for Suicide Prevention (2021, July 28). Suicide statistics. Available online: https:\/\/afsp.org\/suicide-statistics\/#:~:text=In%202019%2C%20the%20suicide%20rates,2018%20to%2020.12%20in%202019."},{"key":"ref_4","unstructured":"OECD (2021, July 25). Suicide Rates (Indicator). Available online: https:\/\/data.oecd.org\/healthstat\/suicide-rates.htm."},{"key":"ref_5","unstructured":"Ministry of Health and Welfare, and Korea Foundation for Suicide Prevention (2021). 2021 White Paper on Suicide Prevention."},{"key":"ref_6","unstructured":"Chiles, J.A., Strosahl, K.D., and Roberts, L.W. (2019). Clinical Manual for the Assessment and Treatment of Suicidal Patients, American Psychiatric Association Publishing. [2nd ed.]."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ko, Y., Han, S.Y., and Jang, H.Y. (2021). Factors Influencing Suicidal Ideation and Attempts among Older Korean Adults: Focusing on Age Discrimination and Neglect. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18041852"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1027\/0227-5910\/a000076","article-title":"A systematic review of elderly suicide prevention programs","volume":"32","author":"Lapierre","year":"2011","journal-title":"Crisis"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2713","DOI":"10.3389\/fpsyg.2018.02713","article-title":"Psychosocial Suicide Prevention Interventions in the Elderly: A Mini-Review of the Literature","volume":"9","author":"Zeppegno","year":"2018","journal-title":"Front. Psychol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e022087","DOI":"10.1136\/bmjopen-2018-022087","article-title":"Models of suicide in elderly: A protocol for a systematic review","volume":"8","author":"Rostami","year":"2018","journal-title":"BMJ Open"},{"key":"ref_11","unstructured":"NICE (2011). Self-Harm in Over 8s: Long-Term Management, NICE."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"97","DOI":"10.4103\/psychiatry.IndianJPsychiatry_272_17","article-title":"Understanding masked depression: A Clinical scenario","volume":"60","author":"Shetty","year":"2018","journal-title":"Indian J. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1038\/s41398-021-01500-w","article-title":"Assessment of suicide attempt and death in bipolar affective disorder: A combined clinical and genetic approach","volume":"11","author":"Monson","year":"2021","journal-title":"Transl. Psychiatry"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Naifeh, J.A., Ursano, R.J., Stein, M.B., Mash, H.B.H., Aliaga, P.A., Fullerton, C.S., Dinh, H.M., Vance, M.C., Wynn, G.H., and Kao, T.C. (2021). Risk of suicide attempt in reserve versus active component soldiers during deployment to the wars in Iraq and Afghanistan. Suicide Life-Threat. Behav.","DOI":"10.1111\/sltb.12770"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1177\/0706743720902655","article-title":"Rurality and Risk of Suicide Attempts and Death by Suicide among People Living in Four English-speaking High-income Countries: A Systematic Review and Meta-analysis","volume":"65","author":"Barry","year":"2020","journal-title":"Can. J. Psychiatry"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1017\/S0033291719001375","article-title":"Characteristics of persons who die on their first suicide attempt: Results from the National Violent Death Reporting System","volume":"50","author":"Jordan","year":"2020","journal-title":"Psychol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1176\/appi.ajp.2018.17101167","article-title":"Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records","volume":"175","author":"Simon","year":"2018","journal-title":"Am. J. Psychiatry"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1001\/jamapsychiatry.2019.0174","article-title":"Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation","volume":"76","author":"Belsher","year":"2019","journal-title":"JAMA Psychiatry"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.comppsych.2018.12.002","article-title":"Characteristics of the \u201cyoung-old\u201d and \u201cold-old\u201d community-dwelling suicidal Ideators: A longitudinal 6-month follow-up study","volume":"89","author":"Park","year":"2019","journal-title":"Compr. Psychiatry"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kessler, R.C., Bernecker, S.L., Bossarte, R.M., Luedtke, A.R., McCarthy, J.F., Nock, M.K., Pigeon, W.R., Petukhova, M.V., Sadikova, E., and VanderWeele, T.J. (2019). The Role of Big Data Analytics in Predicting Suicide. Personalized Psychiatry, Springer.","DOI":"10.1007\/978-3-030-03553-2_5"},{"key":"ref_21","unstructured":"National Health Insurance Service (2021, July 15). National Health Insurance Sharing Service. Available online: https:\/\/nhiss.nhis.or.kr\/bd\/ab\/bdaba000eng.do."},{"key":"ref_22","unstructured":"World Health Organization (2004). ICD-10: International Statistical Classification of Diseases and Related Health Problems: Tenth Revision, World Health Organization. [2nd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s00127-015-1119-x","article-title":"The accuracy of suicide statistics: Are true suicide deaths misclassified?","volume":"51","author":"Bakst","year":"2016","journal-title":"Soc. Psychiatry Psychiatr. Epidemiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1038\/s41398-019-0428-3","article-title":"The prediction of suicide in severe mental illness: Development and validation of a clinical prediction rule (OxMIS)","volume":"9","author":"Fazel","year":"2019","journal-title":"Transl. Psychiatry"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.jad.2019.11.131","article-title":"Predicting suicide: A comparison between clinical suicide risk assessment and the Suicide Intent Scale","volume":"263","author":"Lindh","year":"2020","journal-title":"J. Affect. Disord."},{"key":"ref_26","unstructured":"R Core Team (2016). R: A Language and Environment for Statistical Computing., R Foundation for Statistical Computing."},{"key":"ref_27","unstructured":"RStudio Team (2012). RStudio: Integrated Development for R, RStudio, Inc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.jbi.2018.04.012","article-title":"Automatic address validation and health record review to identify homeless Social Security disability applicants","volume":"82","author":"Erickson","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10916-018-0977-7","article-title":"A System for Automated Determination of Perioperative Patient Acuity","volume":"42","author":"Zhang","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_32","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2017). The Elements of Statistical Learning, Springer. [2nd ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cato, V., Hollandare, F., Nordenskjold, A., and Sellin, T. (2019). Association between benzodiazepines and suicide risk: A matched case-control study. BMC Psychiatry, 19.","DOI":"10.1186\/s12888-019-2312-3"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dodds, T.J. (2017). Prescribed Benzodiazepines and Suicide Risk: A Review of the Literature. Prim. Care Companion CNS Disord., 19.","DOI":"10.4088\/PCC.16r02037"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1097\/NMD.0000000000000889","article-title":"Suicide Methods According to Age and Sex: An Analysis of Data of 239,565 Suicide Victims in the Republic of Korea From 1991 to 2015","volume":"206","author":"Han","year":"2018","journal-title":"J. Nerv. Ment. Dis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s12991-017-0144-4","article-title":"Is low total cholesterol levels associated with suicide attempt in depressive patients?","volume":"16","author":"Messaoud","year":"2017","journal-title":"Ann. Gen. Psychiatry"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.jad.2010.02.111","article-title":"Cholesterol and CSF 5-HIAA in attempted suicide","volume":"125","author":"Asellus","year":"2010","journal-title":"J. Affect. Disord."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2556","DOI":"10.1038\/s41386-018-0162-1","article-title":"Disentangling the genetic overlap between cholesterol and suicide risk","volume":"43","author":"Knowles","year":"2018","journal-title":"Neuropsychopharmacology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1038\/mp.2015.112","article-title":"Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach","volume":"20","author":"Niculescu","year":"2015","journal-title":"Mol. Psychiatry"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.ekir.2020.12.032","article-title":"Stress Related Disorders and the Risk of Kidney Disease","volume":"6","author":"Su","year":"2021","journal-title":"Kidney Int. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Joung, K.I., and Cho, S.I. (2018). Association of low blood pressure with suicidal ideation: A cross-sectional study of 10,708 adults with normal or low blood pressure in Korea. BMC Public Health, 18.","DOI":"10.1186\/s12889-018-5106-5"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.biopsycho.2013.11.005","article-title":"Proneness to worry is negatively associated with blood pressure and baroreflex sensitivity: Further evidence of the blood pressure emotional dampening hypothesis","volume":"96","author":"Delgado","year":"2014","journal-title":"Biol. Psychol."},{"key":"ref_43","unstructured":"Shewhart, W.A., Wilks, S.S., Hosmer, D.W., and Lemeshow, S. (2000). Assessing the Fit of the Model. Applied Logistic Regression, John Wiley & Sons. Chapter 5."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s10979-005-6832-7","article-title":"Comparing effect sizes in follow-up studies: ROC Area, Cohen\u2019s d, and r","volume":"29","author":"Rice","year":"2005","journal-title":"Law Hum. Behav."},{"key":"ref_45","unstructured":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Erlbaum. [2nd ed.]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/s41380-019-0531-0","article-title":"Suicide prediction models: A critical review of recent research with recommendations for the way forward","volume":"25","author":"Kessler","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e7246","DOI":"10.5867\/medwave.2018.05.7246","article-title":"Literature review to identify standardized scales for assessing adult suicide risk in the primary health care setting","volume":"18","author":"Abarca","year":"2018","journal-title":"Medwave"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Large, M., Kaneson, M., Myles, N., Myles, H., Gunaratne, P., and Ryan, C. (2016). Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time. 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