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系统性综述发现非社交媒体心理健康数据集存在差距

一项遵循PRISMA方法学的系统性综述,识别并分析了非源自社交媒体的用于心理健康障碍检测的自由文本数据集。该综述发现,现有的非社交媒体数据集主要为英文,侧重于检测抑郁症,在人口统计学、数据类型和标注技术方面存在差异。研究突显了当前资源的重大差距,并指出了开发更多样化、更可靠和临床相关数据集的机会。 AI

影响 强调了利用NLP和ML为心理健康研究开发更多样化和更可靠数据集的机会。

排序理由 该条目是对arXiv上发表的研究主题可用数据集的系统性综述。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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系统性综述发现非社交媒体心理健康数据集存在差距

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该条目是对arXiv上发表的研究主题可用数据集的系统性综述。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sadiya Sayara Chowdhury Puspo, Ana-Maria Bucur, Stevie Chancellor, \"Ozlem Uzuner, Marcos Zampieri ·

    社交媒体之外的精神健康障碍检测:可用数据集的系统性回顾

    arXiv:2607.03540v1 Announce Type: new Abstract: Detecting mental health disorders in a timely manner is an important societal challenge. NLP and machine learning (ML) methods used to assist with detection rely on data collected primarily from social media. However, such datasets …