萨德克萨伊:使用多种可解释的人工智能技术预测社会焦虑障碍
Krishnaraj Chadaga1, Srikanth Prabhu1, Niranjana Sampathila2
1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
SLAS technology
|March 20, 2024
概括
社会焦虑障碍 (SAD) 可以通过分析患者症状的机器学习模型来诊断. AdaBoost和后勤回归实现了88%的准确性,识别了早期检测的关键诊断因素.
科学领域:
- 精神病学和计算机科学
- 人工智能在心理健康诊断中的应用
背景情况:
- 社会焦虑障碍 (SAD) 或社会恐惧症,涉及对负面判断的持续恐惧,影响社会和职业生活.
- 抑郁症是由环境和生物因素的复杂相互作用引起的.
- 诊断依赖于精神健康障碍诊断和统计手册 (DSM-5) 中概述的标准,考虑身体,情绪和人口统计症状.
研究的目的:
- 研究机器学习 (ML) 技术在诊断社会焦虑障碍 (SAD) 的有效性.
- 开发一种可解释的SAD诊断框架,使用可解释的人工智能 (XAI).
主要方法:
- 利用人口,情绪和身体症状数据进行SAD诊断.
- 应用了多个机器学习分类器,包括AdaBoost和后勤回归.
- 采用四种可解释的人工智能 (XAI) 技术来解释模型预测.
主要成果:
- AdaBoost和后勤回归模型实现了最高的诊断准确率,达到88%.
- XAI的分析确定了"利博维茨社会焦虑量表问卷"和"公开演讲的恐惧"作为关键的诊断属性.
- 该研究建立了一个临床决策支持系统框架,用于SAD识别.
结论:
- 机器学习模型在诊断社会焦虑障碍方面表现出很高的准确性.
- XAI提高了基于ML的SAD诊断的解释性,突出了关键的贡献因素.
- 开发的框架在教育,医疗保健和职业环境中对早期SAD检测有潜在的应用.
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