由人工智能驱动的心理健康应用程序与数据隐私保护数据隐私保护
Pooja Bagane1, Anushree Dahiya1, Shardul Kacheria1
1Symbiosis Institute of Technology -Pune Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX
|January 9, 2026
概括
本研究介绍了一种使用自然语言处理 (NLP) 来检测用户在文本中的情绪的人工智能框架,通过安全的隐私优先技术增强数字心理健康支持. 它提高了情感识别,以更好地照顾用户.
科学领域:
- 数字心理健康数字心理健康
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 在现代社会中,焦虑障碍,情绪状况和压力的患病率越来越高.
- 对提供支持的数字心理健康技术的高需求.
- 需要保护隐私的情绪识别系统.
研究的目的:
- 提出一个人工智能驱动的框架,用于用户文本中的情感识别.
- 整合自然语言处理 (NLP) 与隐私保护系统.
- 通过精确的情绪检测来增强数字心理健康支持.
主要方法:
- 使用BERT-base-uncased和ModernBERT大型变压器模型进行多标签的情感分类.
- 实施AES-256加密,Supabase身份验证和基于角色的访问控制,以确保数据安全.
- 分析情绪中的阶级不平衡,并评估精确回忆的权衡.
主要成果:
- 现代BERT表现出快速收和提高对刺和混合情绪状态的敏感性.
- 开发了一个以隐私为首要的架构,用于与数字治疗和临床决策软件集成.
- 设计了一个情绪反循环,用于上下文响应和个性化正念.
结论:
- 拟议的AI框架使用先进的NLP模型有效地识别文本中的情绪.
- 隐私优先架构确保数据安全,并促进与现有数字健康解决方案的整合.
- 该系统为增强的,个性化的数字心理健康干预提供了潜力.
关键词:
匿名通信是一种匿名的沟通方式.伯特的基准是未公布的.二元交叉 (BCE) 损失情绪检测 情绪检测 情绪检测在 GoEmotions 数据库中.心理健康 心理健康正念练习可以练习正念.现代BERT大大的多个标签分类的分类.自然语言处理 (NLP)反应 反应 反应 反应安全的数据处理数据处理.这就是Supabase的基础.尾风CSS 在线阅读更多相关视频
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