评估自然语言处理的衍生语言特征与当前的自杀想法,过去的尝试和未来的自杀行为相关
Lauren McBride1, Varsha D Badal2, Philip D Harvey3
1San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA.
自然语言处理 (NLP) 有效地预测了患有精神病的个人的未来自杀行为,使用来自二元任务的语言特征. 这种方法对理解和评估这一群体的自杀风险有希望.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算语言学 计算语言学
- 在医疗保健中的数据科学.
背景情况:
- 与普通人群相比,患有精神病的个体具有较高的自杀风险.
- 自然语言处理 (NLP) 已被应用于精神病研究,但不是用于预测未来的自杀行为.
- 这项研究调查了NLP衍生的语言特征,用于心理疾病中自杀风险评估.
研究的目的:
- 为了确定NLP衍生的语言特征从二次任务可以预测自杀念头,过去的尝试,和未来的自杀行为在成年人的精神障碍.
- 评估NLP模型对各种自杀相关结果的预测性能.
- 在这个人群中确定与自杀风险相关的关键语言特征.
主要方法:
- 有112名患有精神疾病的成年人完成了自杀严重程度尺度和双向角色扮演任务.
- 语言特征 (词汇,多样性,情感) 通过NLP从任务记录中提取出来.
- 机器学习模型 (MLPRegressor) 被训练来预测自杀结果,使用SHAP进行特征分析.
主要成果:
- 观察到自杀念头 (42.9%),过去的尝试 (67.9%) 和未来的自杀行为 (13.3%) 的高患病率.
- 对于过去的尝试 (F1=0.75) 和当前的想法 (F1=0.74-0.79),NLP模型显示出强大的预测性能.
- 未来自杀行为模型实现了最高的预测准确性 (F1=0.86-0.93).
结论:
- 从二元交互中获得的NLP衍生的语言特征显示了精神病患者未来自杀行为的高预测准确性.
- 这些发现表明,NLP对二次任务的分析可以提高对自杀风险的理解.
- 需要进一步复制以验证这些有希望的结果用于临床应用.
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