开发一种基于机器学习的正负综合征尺度缩写形式
Gong-Hong Lin1, Jen-Hsuan Liu2, Shih-Chieh Lee3
1International Ph.D. Program in Gerontology and Long-Term Care, College of Nursing, Taipei, Taiwan.
Asian journal of psychiatry
|February 23, 2024
概括
开发了一种新的基于机器学习的正负综合征量表 (PANSS-MLSF) 的简体形式. 该工具减少了管理时间,同时保持得分可比性和有效性,并可以估计删除项目的得分.
科学领域:
- 精神病学和计算神经科学
- 开发和验证新的评估工具.
- 人工智能在临床心理学中的应用.
背景情况:
- 阳性和阴性综合征量表 (PANSS) 是一种全面的精神病学评估工具.
- 目前的PANSS管理和评分可能需要很长的时间,最多需要50分钟.
- 需要有效和可靠的方法来评估精神病症状.
研究的目的:
- 开发和验证基于机器学习的PANSS (PANSS-MLSF) 的简体形式.
- 为了确保PANSS-MLSF准确地复制原来的PANSS分数.
- 为了使得从完整的PANSS尺度中删除的项目可以估计得分.
主要方法:
- 使用人工神经网络开发PANSS-MLSF.
- 使用极端梯度提升分类器估计删除项目的得分.
- 评估可靠性 (克朗巴赫的alpha),并发有效性 (皮尔森的r与PANSS) 和收有效性 (皮尔森的r与CGI-S,MMSE,劳顿IADL).
- 使用科恩的卡帕评估删除项目的得分协议.
主要成果:
- 这项研究包括573名中度重症患者.
- 提出了两种PANSS-MLSF版本:一个有15个项目,另一个有9个项目.
- PANSS-MLSF得分与原来的PANSS得分具有很高的相似性 (平均平方误差为2.6-24.4).
- 对PANSS-MLSF.观察到良好的可靠性,并发有效性和收有效性.
- 对于60%的估计删除项目的得分,发现了中等到良好的协议.
结论:
- PANSS-MLSF提供了一种切实可行的解决方案,可以缩短PANSS的管理时间.
- 简短的形式保持得分可比性,并维护可靠性和有效性.
- PANSS-MLSF可以有效地估计删除项目的得分,提供进一步的实用性.
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