在雅安地震后,在早期青少年中使用文本挖掘技术进行创伤后应激障碍的自动查
Yuzhuo Yuan1,2, Zhiyuan Liu2, Wei Miao2
1Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
Frontiers in psychiatry
|December 26, 2024
概括
使用文本挖掘分析自我叙述可以自动化青少年的创伤后应激障碍 (PTSD) 查. 这种方法有助于创伤幸存者的早期发现和治疗计划.
科学领域:
- 计算精神病学是一种计算精神病学.
- 心理健康中的自然语言处理.
- 创伤研究的研究.
背景情况:
- 自我叙述提供了对创伤的洞察力,但在临床查中未得到充分利用.
- 自我叙述的自动分析可以改善早期识别心理健康状况.
研究的目的:
- 开发一种自动化方法来选早期青少年地震幸存者的创伤后应激障碍 (PTSD).
- 分析自我叙述,以便及时有效地进行PTSD查.
主要方法:
- 收集了430名青少年地震幸存者的自我叙述,使用基于调查的问卷.
- 采用文本分类模型 (BERT,SVM,KNN) 来识别PTSD症状和叙述中的行为指标.
- 使用DSM-5 (PCL-5) 的PTSD检查清单进行并发评估.
主要成果:
- 在句子层面上对PTSD症状进行分类,获得了73.2%的准确性.
- 在分类与PTSD相关的行为指标方面达到67.2%的准确性.
- 伯特模型在文本分类方面表现出卓越的表现.
结论:
- 自我叙述与文本挖掘相结合,为自动化,快速和准确的PTSD查提供了一个有希望的途径.
- 这种方法可以通过向临床医生提供PTSD症状和指标的证据来提高早期检测和治疗计划.
- 在社区和学校环境中进行查可以提高干预措施的可访问性和及时性.
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