在线持续学习的用户在社交媒体上的自杀风险
Lei Cao1, Ling Feng2, Yang Ding2
1Faculty of Psychology, Beijing Normal University, Beijing, 100875, China; Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China.
Artificial intelligence in medicine
|June 29, 2025
概括
这项研究引入了一种使用社交媒体数据的新型持续自杀风险评估模型. 三层网络可实现实时,自适应性风险跟踪,达到96%的准确性.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 心理健康技术 心理健康技术
背景情况:
- 社交媒体的使用是无处不在的,需要新的方法来评估自杀风险.
- 现有的批量学习模型不适合社交媒体数据流的动态性.
- 实时,持续评估对于及时干预至关重要.
研究的目的:
- 为社交媒体用户开发一个持续的自杀风险评估模型.
- 为了解决静态,批量学习方法的局限性.
- 为了实现个性化和适应性风险跟踪随着时间的推移.
主要方法:
- 一个三层的联合记忆网络,包含短期的个人记忆,长期的个人记忆和全球记忆.
- 从用户的情绪和行为动态中不断学习.
- 整合持续捕获的个人特征,用于风险评估.
主要成果:
- 拟议的模型支持实时,持续的学习,不需要全面的再培训.
- 在190名社交媒体用户 (95名自杀者,95名非自杀者) 的数据集上实现了96%的准确性.
- 证明了个性化和适应性风险跟踪能力.
结论:
- 新型网络架构为社交媒体上的自杀风险评估提供了及时且可扩展的解决方案.
- 持续学习和个性化功能集成提高了风险检测的准确性和适应性.
- 这种方法有望通过社交媒体进行主动的心理健康干预.
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