多重WD:社交媒体帖子中的多标签健康维度.
Muskan Garg1, Xingyi Liu1, M S V P J Sathvik2
1Mayo Clinic, Rochester, 55901 MN, USA.
Journal of biomedical informatics
|January 8, 2024
概括
这项研究使用人工智能识别了在线文章中的健康维度. 精心调整的语言模型在用户生成的文本中检测社会和心理健康指标方面表现强.
科学领域:
- 计算语言学 计算语言学
- 数字健康数字健康
- 社交计算社会计算
背景情况:
- 哈尔伯特·L·恩 (Halbert L. Dunn) 的健康理念包括社会和心理健康.
- 忽视这些方面可能会对心理健康产生负面影响.
- 未经治疗的精神障碍可能会升级为严重的疾病.
研究的目的:
- 开发一种细粒度的方法来识别人类书面文本中的健康维度.
- 在社交媒体上检测社会和心理福祉的指标.
- 分析自我叙述的著作,以发现健康维度的存在.
主要方法:
- 介绍MultiWD数据集 (3281个实例) 用于健康维度的识别.
- 在多标签分类任务中使用最先进的分类器.
- 利用微调的大型语言模型和BERT进行文本分析.
主要成果:
- 精心调整的大型语言模型表现出卓越的性能.
- 伯特模型建立了一个基线,达到F1得分76.69.
- 对比分析强调了先进语言模型的有效性.
结论:
- 人工智能模型需要可靠的,特定于领域的知识来准确地提取健康维度.
- 开发具有上下文意识的AI对于全面的心理健康分析至关重要.
- 该研究强调了人工智能在理解数字健康指标方面的潜力.
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