一种机器学习方法,使用与移民有关的文化压力来对风幸存者进行抑郁症和创伤后应激障碍的分类
Nathaniel A Dell1, Christopher P Salas-Wright2, Michael G Vaughn3
1Washington University School of Medicine in St. Louis, Department of Psychiatry, United States of America.
Journal of affective disorders
|November 22, 2023
概括
文化压力因素对风玛丽亚幸存者的抑郁症和PTSD分类产生重大影响. 机器学习模型强调了对移民的文化敏感护理的需要.
科学领域:
- 行为健康研究 行为健康研究
- 数据科学应用程序数据科学应用程序
- 在健康方面存在差异.
背景情况:
- 调查行为健康中的文化压力因素.
- 检查风玛丽亚幸存者的抑郁症和创伤后应激障碍 (PTSD).
- 评估从波多黎各到美国大陆的移民之间的健康差异.
研究的目的:
- 评估文化压力因素在对抑郁症和创伤后应激障碍的分类中的重要性.
- 比较随机森林 (RF) 和后勤回归 (LR) 模型的性能.
- 确定关键变量,以准确地分类移民人口中的心理健康状况.
主要方法:
- 使用随机森林 (RF) 和后勤回归 (LR) 来进行分类.
- 在模型中包括人口统计,风暴露和文化压力变量.
- 使用AUC,精度,平衡精度,F1得分,精度,回忆和特异性来评估模型性能.
主要成果:
- 负面接收背景和语言压力因素适度改善了抑郁症和PTSD分类.
- 与LR相比,RF模型在两个条件下都在准确性,特异性和精度方面表现出卓越的性能.
- 射频显示出更高的平衡精度和F1分数用于抑郁症分类.
结论:
- 文化和语言因素对于移民人口中精确的心理健康分类至关重要.
- 文化和语言相适应,创伤知情的临床服务对于最近的移民至关重要.
- 将迁移前和迁移后的压力因素纳入评估中,可以增强针对行为健康挑战的临床实践.
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