在社交媒体上检测抑郁症症状的可解释性
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Eliseo Bao1, Anxo Pérez1, Javier Parapar1
1Information Retrieval Lab (IRLab), Centro de Investigación en Tecnoloxías da Información e da Comunicación (CITIC), Campus de Elviña, 15071 A Coruña, Galicia Spain.
Health information science and systems
|September 9, 2024
概括
本研究介绍了基于变压器的模型,用于检测和解释社交媒体内容中的抑郁症状. 这些模型提供了与临床症状一致的自然语言解释,提高了心理健康专业人员的信任和可解释性.
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
- 医疗保健中的人工智能
背景情况:
- 社交媒体平台越来越多地用于讨论心理健康.
- 现有的心理健康检测模型往往缺乏可解释性,阻碍了临床的信任.
- 临床标志物和症状一样,对于提高计算模型的解释性至关重要.
研究的目的:
- 开发基于变压器的架构,用于检测和解释社交媒体内容中的抑郁症状标志物.
- 将单独的分类和解释模型与统一的模型进行比较.
- 调查对话式大型语言模型 (LLM) 对此任务的实用性.
主要方法:
- 开发基于变压器的模型,用于联合或单独的分类和解释.
- 利用上下文学习和微调与对话的LLMs.
- 使用以症状为重点的数据集,离线指标和专家在循环中的评估进行评估.
主要成果:
- 模型成功地检测到社交媒体内容中的抑郁症状标志物.
- 生成的自然语言解释与验证的临床症状保持一致.
- 统一模型和LLM方法在平衡分类准确性和可解释性方面表现有前途.
结论:
- 基于变压器的模型可以在社交媒体上有效地检测和解释抑郁症状.
- 可解释的,基于症状的解释增强了临床医生计算心理健康工具的实用性.
- 这项研究有助于建立更值得信赖和临床相关的心理健康AI.
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