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机器学习可以从儿童和青少年的创伤叙述中预测PTSD症状吗?

Alessandra Giuliani¹, Tamsin Sharp¹, Yeukai Chideya²

¹Department of Psychology, University of Bath, Bath, UK.

European journal of psychotraumatology

|December 2, 2025

概括

机器学习模型很难预测儿童的创伤后应激障碍 (PTSD) 症状,仅使用创伤叙述. 然而,包括自我报告的记忆特征显著提高了对青少年PTSD风险的预测准确度.

科学领域:

计算精神病学是一种计算精神病学.
发展心理学是发展心理学.
机器学习在心理健康中的应用

背景情况:

机器学习 (ML) 越来越多地被用于识别心理健康状况.
创伤叙事特征与创伤后应激障碍 (PTSD) 的发展有关.
从儿童的创伤叙述中预测PTSD是研究不足的.

研究的目的:

用创伤叙述来评估ML模型预测青少年PTSD症状严重程度的能力.
为了确定单独的叙事特征是否可以预测儿童的PTSD.
评估结合自我报告的创伤记忆特征对预测性能的影响.

主要方法:

训练和微调两个预训练 (例如,RoBERTa) 和两个基准ML模型.
利用了来自英国和南非的400名儿童 (年龄为7-17岁) 的自传性创伤叙述.
与自我报告的创伤记忆特征 (无组织,生动性) 进行模型性能比较.

主要成果:

机器学习模型在数据集中仅使用创伤叙述的预测性能较差.
整合自我报告的特征 (无组织,生动性) 显著改善了基准模型的性能.
增强的性能在英国数据集中尤其显著 (例如,LASSO R2=.57).

结论:

创伤叙述本身对于预测儿童PTSD症状的价值有限.
主观创伤记忆特征大大提高了ML模型对PTSD风险的预测能力.
专注于自我报告的数据对于为年轻人开发可扩展的自动化PTSD风险预测工具至关重要.

关键词:

自动学习是一种自动学习.在法律上,LLMs.机器学习机器学习创伤后应激障碍 (PTSD) 是一种这就是TEPTT的意思.儿童和青少年儿童和青少年大型语言模型 (LLM)预测模型预测模型创伤的叙述创伤的叙述儿童和青少年儿童和青少年预测模型的预测模型.创伤的叙述创伤的叙述

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