Am J Epidemiol. 2021 Dec 1;190(12):2517-2527. doi: 10.1093/aje/kwab112.
Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Danish nationwide registry data. Cases were all persons who made a nonfatal suicide attempt between 1995 and 2015 (n = 22,974); the subcohort was a 5% random sample of the population at risk on January 1, 1995 (n = 265,183). We developed sex-stratified classification trees and random forests using 1,458 predictors, including demographic factors, family histories, psychiatric and physical health diagnoses, surgery, and prescribed medications. We found that substance use disorders/treatment, prescribed psychiatric medications, previous poisoning diagnoses, and stress disorders were important factors for predicting suicide attempts among men and women. Individuals in the top 5% of predicted risk accounted for 44.7% of all suicide attempts among men and 43.2% of all attempts among women. Our findings illuminate novel risk factors and interactions that are most predictive of nonfatal suicide attempts, while consistency between our findings and previous work in this area adds to the call to move machine learning suicide research toward the examination of high-risk subpopulations.
自杀未遂是全球范围内导致伤害的主要原因。准确预测自杀未遂可能为预防提供机会。本病例-队列研究使用机器学习方法，在丹麦全国登记数据中，研究了自杀未遂的性别特异性风险特征。病例是指在 1995 年至 2015 年间发生非致命性自杀未遂的所有人（n=22974）；子队列是 1995 年 1 月 1 日风险人群的 5%随机样本（n=265183）。我们使用 1458 个预测因子（包括人口统计学因素、家族史、精神和身体健康诊断、手术和处方药物），为男性和女性分别开发了分层分类树和随机森林。我们发现，物质使用障碍/治疗、处方精神药物、既往中毒诊断和应激障碍是预测男性和女性自杀未遂的重要因素。预测风险最高的前 5%的个体占男性自杀未遂总数的 44.7%，占女性自杀未遂总数的 43.2%。我们的研究结果阐明了预测非致命性自杀未遂的新的风险因素和相互作用，而我们的研究结果与该领域之前的研究工作的一致性，也进一步呼吁将机器学习自杀研究转向高危亚人群的研究。
