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利用自然语言处理精确预防青少年心理健康障碍:系统性审查

Sheriff Tolulope Ibrahim¹, Madeline Li², Jamin Patel³

¹DEPtH Lab, School of Health Studies, Faculty of Health Sciences, Western University, London, Ontario, N6A 5B9, Canada; Children's Health Research Institute, Lawson Health Research Institute, 750 Base Line Road East, Suite 300, London, Ontario, N6A 5B9, Canada.

Computers in biology and medicine

|February 22, 2025

概括

自然语言处理 (NLP) 在早期发现青少年精神健康障碍方面表现有前途. 这次审查突出了NLP的重点.

科学领域:

数字健康数字健康
计算精神病学是一种计算精神病学.
青少年心理健康青少年心理健康

背景情况:

全球心理健康危机影响着年轻人,需要创新的解决方案.
精确预防策略可以识别个别风险,进行量身定制的干预.
自然语言处理 (NLP) 显示出早期发现精神健康障碍的潜力.

研究的目的:

审查NLP在年轻人中检测心理健康障碍的应用.
确定NLP在改善早期检测和个性化的青少年心理健康护理方面的作用.
假设NLP能够提高青少年心理健康护理的早期发现和个性化护理的能力.

主要方法:

从5个数据库中选的1197篇文章的系统审查.
包括12篇论文,涵盖心理健康障碍,数据源,NLP目标,注释,语言标记和表现.
使用霍克的检查清单评估研究质量.

主要成果:

大多数研究都集中在自杀风险 (42%),抑郁症 (25%) 和压力 (17%) 上.
社交媒体 (42%) 和采访 (33%) 是主要的数据来源;语言查询和词数 (LIWC) 和支持矢量机器 (SVM) 是常见的分析工具.
探索性研究占主导地位,其中有一个实时检测工具;验证指标显示出强大的预测性能.

结论:

尽管存在诸如偏见和数据质量等挑战,NLP对青少年心理健康检测具有重大潜力.
未来的研究应该专注于多样化,多式联运数据集,解决数据不平衡,并增强实时检测.
推进NLP驱动的干预需要探索变压器模型并确保道德,包容的数据处理.

关键词:

数字平台数字平台以人为中心的人工智能机器学习是机器学习.心理健康障碍心理健康障碍多式联运数据多式联运数据自然语言处理自然语言处理.精确的预防方法.

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