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小矮人更新了人优化算法,用于从社交媒体中检测抑郁和自杀
Divya Agarwal1, Vijay Singh2, Ashwini Kumar Singh1
1Department of Computer Science and Engineering, Graphic Era Deemed to Be University, 566/6 Bell Road, Clement Town, Dehradun, Uttarakhand, 248002, India.
The Psychiatric quarterly
|February 13, 2025
概括
这项研究引入了一种用于从社交媒体数据中检测抑郁症和自杀念头的新方法. 通过使用先进的深度学习和优化技术,SADDSM方法提高了准确性.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 机器学习 机器学习
背景情况:
- 抑郁症和自杀念头是全球关键的健康问题.
- 对这些疾病的临床评估面临着可访问性和耻辱障碍.
- 现有的计算方法在数据变化和模型概括方面遇到了困难.
研究的目的:
- 从社交媒体 (SADDSM) 开发一种新的自杀和抑郁症检测方法.
- 解决心理健康检测中的数据变异性和模型概括性的挑战.
- 提高自动心理健康监测的准确性和可靠性.
主要方法:
- 数据的预处理包括停止词的删除,标记化和干燥.
- 功能提取利用了TF-IDF,风格特征和增强的word2vec.
- 对于特征融合,使用了修改后的相互信息分数.
- 开发了RNN,DBN和改进的LSTM的整体模型.
- 小矮人更新了优化算法 (DU-POA),优化了模型重量.
主要成果:
- SADDSM 模型以 90% 的培训数据实现了 0.962 的准确率.
- 整体深度学习方法在现有技术上表现出卓越的性能.
- DU-POA有效地微调模型重量,以提高预测能力.
结论:
- 提议的SADDDSM方法为从社交媒体中检测抑郁症和自杀念头提供了强大而准确的解决方案.
- 集成先进的特征提取,集体深度学习和优化算法的集成显著提高了检测能力.
- 这项研究强调了社交媒体分析对于可扩展和可访问的心理健康监测的潜力.
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