在基于社区的社交媒体帖子中识别使用,滥用和成的自我披露
Chenghao Yang1, Tuhin Chakrabarty2, Karli R Hochstatter3
1University of Chicago.
概括
从社交媒体中识别阿片类药物使用障碍阶段是具有挑战性的. 在模型中使用解释可以显著提高这一公共卫生紧急情况的分类准确性.
科学领域:
- 公共卫生 公共卫生
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
背景情况:
- 在美国,阿片类药物过量是国家公共卫生紧急情况,导致超过50万人死亡.
- 识别患有阿片类药物使用障碍 (OUD) 风险的患者需要医疗从业人员提供有效的工具.
- 像Reddit这样的社交媒体平台通过用户自我披露提供了对敏感药物相关行为的洞察力.
研究的目的:
- 从Reddit帖子中开发和评估用于分类阿片类药物使用的六个阶段 (医疗使用,滥用,成,恢复,复发,不使用) 的模型.
- 调查跨度级提取性解释对OUD阶段分类中的注释质量和模型性能的影响.
- 评估OUD连续分析的监督,少数射击和零射击学习设置中最先进模型的有效性.
主要方法:
- 收集并注释了2500个与阿片类药物相关的Reddit帖子,标记了阿片类药物使用的六个不同的阶段.
- 对每个职位进行注释的跨度层次提取性解释,以增强理解和模型培训.
- 评估了各种最先进的机器学习模型,使用监督,少数射击和零射击学习方法.
主要成果:
- 将阿片类药物使用障碍的阶段分类取决于背景,并带来了重大挑战.
- 在模型开发过程中包含解释导致了分类准确度的大幅增加.
- 错误分析强调了识别OUD阶段的复杂性和上下文性质.
结论:
- 在OUD研究等高风险领域,跨度层次的解释有助于提高模型性能.
- 机器学习模型,特别是当加上解释时,显示出分析阿片类药物使用障碍连续性的希望.
- 这种方法可以帮助开发更好的工具来识别有风险的患者和了解OUD进展.
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