论文标题
与青少年自杀未遂的风险不断增长相关的健康行为：一项数据驱动的横断面研究
Health-behaviors associated with the growing risk of adolescent suicide attempts: A data-driven cross-sectional study
论文作者
论文摘要
目的：确定并检查健康行为与青少年自杀未遂风险增加的关联，同时控制社会经济和人口统计学差异。设计：使用横截面数据进行数据驱动分析。环境：1999年至2017年蒙大拿州的社区。主题：选定的22,447名青少年，其中1,631名青少年至少尝试自杀一次。措施：考虑了心理行为，非法物质消费，学校的日常活动和人口背景的总体29个变量（预测指标）。分析：使用机器学习算法的库以及传统使用的逻辑回归库来建模和预测自杀尝试风险。使用准确性，精度，召回和F得分度量标准测量模型性能（拟合度和预测精度）。结果：非参数贝叶斯树合奏模型的表现优于所有其他模型，其合适性的精度为80.0％（F-SCORE：0.802），预测精度为78.2％（F-SCORE：0.785）。确定的主要健康行为包括：悲伤/无望，随后在学校的安全问题，体育斗争，吸入毒品使用，在学校的非法药物消费，当前的香烟使用情况以及在小时候（低于15岁）的首次性行为。此外，还发现少数群体（美洲印第安人/阿拉斯加人，西班牙裔/拉丁裔）和女性也很容易自杀。结论：这项工作的重大贡献是了解关键的健康行为和健康差异，从而导致青少年自杀率较高，同时考虑结果之间的非线性和复杂相互作用和暴露变量。
Purpose: Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socioeconomic and demographic differences. Design: A data-driven analysis using cross-sectional data. Setting: Communities in the state of Montana from 1999 to 2017. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once. Measures: Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds, were considered. Analysis: A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances (goodness-of-fit and predictive accuracy) were measured using accuracy, precision, recall and F-score metrics. Results: The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score:0.802) and 78.2% in predictive accuracy (F-score:0.785). Key health-behaviors identified include: being sad/hopeless, followed by safety concerns at school, physical fighting, inhalant usage, illegal drugs consumption at school, current cigarette usage, and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos), and females are also found to be highly vulnerable to attempting suicides. Conclusion: Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables.
