使用机器学习预测弗吉尼亚州东南部非洲裔美国人的心理健康差异
Ismail El Moudden1, Michael C Bittner1, Matvey V Karpov1
1Eastern Virginia Medical School (EVMS), Norfolk State University, Norfolk, VA, USA.
Scientific reports
|February 18, 2025
概括
这项研究强调了非洲裔美国人的显著心理健康差异,情绪障碍是最常见的. 人工智能模型确定了性别和年龄等关键风险因素,表明弗吉尼亚州东南部的负担更高.
科学领域:
- 公共卫生 公共卫生
- 医疗保健中的人工智能
- 心理健康研究 心理健康研究
背景情况:
- 非洲裔美国人面临着严重的心理健康差异.
- 人工智能和机器学习为分析健康结果数据提供了新的方法.
- 了解人口和临床风险因素对于有针对性的干预至关重要.
研究的目的:
- 为了检查弗吉尼亚州东南部非洲裔美国人的心理健康差异.
- 用人工智能和机器学习来预测心理健康障碍的结果.
- 确定这一群体中精神健康障碍的主要人口和临床预测因素.
主要方法:
- 分析了弗吉尼亚州东南部 (2016-2020) 非洲裔美国成年人 (18-85岁) 的回顾性数据.
- 应用和验证各种机器学习模型 (梯度提升,随机森林,神经网络,后勤回归,纯粹贝叶斯) 使用100次重复的5倍交叉验证.
- 开发名ograms以可视化风险因素.
主要成果:
- 情绪影响性障碍 (41.66%) 和精神分裂谱和其他精神病障碍是最常见的.
- 女人主要经历情绪障碍;年龄在30-40岁是常见的.
- 渐变增强显示出优异的预测性能;性别,年龄,并发症和保险类型是关键预测因素.
- 与全国平均水平相比,观察到更高的精神健康障碍患病率.
结论:
- 弗吉尼亚州东南部的非裔美国人表现出潜在的更大的心理健康负担.
- 人工智能和机器学习模型有效地预测心理健康结果并识别风险因素.
- 调查结果强调了需要针对性干预来解决这一人口群体中心理健康差异的需要.
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