在人工智能背景下,基于先验算法预测学生的心理健康风险
You Fu1, Fang Ren2, Jiantao Lin3
1School of Computer Engineering, Shanxi Vocational University of Engineering Science and Technology, Jinzhong, China.
Frontiers in public health
|February 27, 2025
概括
人工智能 (AI) 可以使用混合Prophet-LSTM模型预测大学生心理健康风险. 这种方法有助于早期干预,并改善大学心理健康服务.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 高等教育 高等教育
背景情况:
- 大学生中不断增加的心理健康问题需要先进的识别方法.
- 传统的方法可能无法充分解决学生心理健康的复杂性.
研究的目的:
- 研究人工智能 (AI) 在预测大学生心理健康风险方面的有效性.
- 开发和评估一种新的混合预测模型,用于早期检测心理困扰.
主要方法:
- 开发了一种混合Prophet-LSTM模型,将Prophet时间序列预测与长短期记忆 (LSTM) 网络集成在一起.
- 协会规则 (先验算法) 确定了关键的心理健康风险因素.
- 量子粒子群集优化 (QPSO) 优化了模型参数.
- 用中国大学生心理健康调查数据验证了模型的性能.
主要成果:
- 先知-LSTM模型在预测学生心理健康风险方面明显优于其他机器学习算法.
- 通过评估指标证实了高准确度,包括心理问题的检测率和缺失.
结论:
- 人工智能驱动的预测模型显示,早期识别有风险的学生具有很大的潜力.
- 研究结果支持将人工智能整合到高等教育中,以加强心理健康服务.
- 未来的工作应该探索实时数据和个性化干预.
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