使用人工智能检测自杀风险:创造一个基准数据集的承诺,用于对自杀风险的检测进行研究
Mahboobeh Parsapoor Mah Parsa1, Jacob W Koudys2, Anthony C Ruocco2,3
1Speech and Data Science Groups, CRIM - Centre de Recherche Informatique de Montréal, Montreal, QC, Canada.
Frontiers in psychiatry
|August 11, 2023
概括
这项研究建议将自杀理论整合到人工智能驱动的工具中,以更好地检测自杀风险. 目标是创建可靠的语音和基于语言的系统,以识别高风险的个人.
科学领域:
- 精神病学是一个精神病学.
- 计算机科学 计算机科学
- 语言学的语言学.
背景情况:
- 自杀是一个主要的公共卫生问题,需要有效的风险检测工具.
- 目前基于人工智能的自杀风险检测系统往往缺乏理论依据.
- 现有的自杀理论很复杂,很难直接在AI模型中实现.
研究的目的:
- 提出一种开发基于语音和语言的自杀风险检测系统的方法.
- 为应对将各种自杀理论纳入人工智能的挑战.
- 建立一个可靠的人工智能驱动自杀风险评估的基础.
主要方法:
- 开发基于语音和语言的AI系统,用于自杀风险检测.
- 强调创建一个基准文本和语音数据集.
- 提出研究设计,以区分自杀念头和自杀企图的风险因素.
主要成果:
- 拟议的方法旨在提高自杀风险检测的准确性和可靠性.
- 一个标准化的数据集将使可靠的机器学习和深度学习模型的开发成为可能.
- 区分构想与尝试的风险因素对于有针对性的干预至关重要.
结论:
- 将自杀理论整合到人工智能中可以增强自杀风险检测系统.
- 标准化的基准数据集对于开发可靠的语音和基于文本的检测系统至关重要.
- 这项研究为预防自杀的先进AI应用奠定了基础.
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