在无家可归的年轻人中识别大麻使用行为 使用基于机器学习的框架:开发和评估研究
Tianjie Deng1, Andrew Urbaczewski1, Young Jin Lee1
1Department of Business Information & Analytics, Daniels College of Business, University of Denver, Denver, CO, United States.
JMIR AI
|October 17, 2024
概括
无家可归的年轻人的社交媒体数据可以预测大麻的使用情况,有助于早期干预. 这种机器学习方法可以识别有风险的个人,以提供有针对性的支持.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生信息学 公共卫生信息学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 无家可归的年轻人呈现出不成比例的高物质使用率 (69%的依赖).
- 结构和社会不平等加剧了物质使用的挑战,并阻碍了这一群体获得护理的机会.
- 现有的差异需要创新的方法来识别和支持处于危险的无家可归青年.
研究的目的:
- 开发一个机器学习框架来预测无家可归的年轻人的物质使用 (大麻概率).
- 利用社交媒体内容 (帖子,互动) 进行风险预测.
- 使社会工作者和护理人员能够主动识别和协助高风险个人.
主要方法:
- 招募了133名无家可归的年轻人,收集了1年的社交媒体数据,并进行了调查.
- 利用社交共享情绪和社会支持理论来识别预测特征.
- 应用自然语言处理 (NLP) 来从社交媒体数据中提取机器学习模型的特征.
主要成果:
- 机器学习模型仅使用社交媒体数据实现了0.72的曲线下的面积和0.81的准确性.
- 模型通过评估性别和年龄段的虚假阳性率来证明公平性,避免调查数据的偏差.
- 预测性能的验证没有纳入潜在偏见的调查信息.
结论:
- 无家可归的年轻人和他们的同龄人之间的社交媒体互动对于预测物质使用非常有价值.
- 开发的框架允许有效地分配资源给脆弱的年轻人,并尽量减少开支.
- 这种方法可以扩展到预测这个人群中的其他健康行为.
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