可解释的基于人工智能的自杀和非自杀思想检测来自社交媒体文本的增强合奏技术
Daniyal Alghazzawi1, Hayat Ullah2, Naila Tabassum2
1Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Scientific reports
|January 8, 2025
概括
这项研究引入了一个可解释的AI框架,使用组合方法准确检测社交媒体上的自杀念头. 该方法通过提供对在线内容的可解释的见解来增强自杀预防.
科学领域:
- 人工智能的人工智能
- 计算社会科学 计算社会科学
- 心理健康技术 心理健康技术
背景情况:
- 在社交媒体上识别自杀念头对于预防自杀至关重要.
- 传统的人工智能模型在决策过程中缺乏透明度.
- 可解释的人工智能 (XAI) 提供了可解释性,对于理解人工智能驱动的分类至关重要.
研究的目的:
- 开发一个新的框架来区分社交媒体上的自杀和非自杀想法.
- 将可解释AI (XAI) 与整体机器学习方法集成,以提高准确性和可解释性.
- 提高人工智能系统的可靠性,用于监控和干预在线自杀相关讨论.
主要方法:
- 利用组合技术,结合多个机器学习算法.
- 集成可解释AI (XAI) 来分析和解释模型分类.
- 对各种社交媒体数据集的框架与最先进的方法进行了评估.
主要成果:
- 与现有方法相比,拟议的框架在检测自杀性内容方面表现出更高的准确性.
- 实现了高绩效指标:F1得分为95.5%的自杀念头和99%的非自杀念头.
- 该XAI组件提供了对驱动模型分类的特征的清晰洞察.
结论:
- 开发的框架为在线识别自杀念头提供了更可靠和更易于解释的方法.
- 这项研究弥合了人工智能在心理健康应用中的性能和可解释性之间的差距.
- 这些发现支持使用可解释的人工智能及时干预自杀预防工作.
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