分析生成性人工智能和机器学习在自我评估精神分裂症的负面症状
Chih-Min Liu1,2, Yi-Hsuan Chan3, Ming-Yang Ho4
1Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan.
Schizophrenia bulletin
|August 13, 2025
概括
这项研究表明,生成性AI (GenAI) 和机器学习 (ML) 可以准确评估精神分裂症.
科学领域:
- 精神病学和计算神经科学
- 医疗保健中的人工智能
- 临床评估工具 临床评估工具
背景情况:
- 传统的精神分裂症负面症状评估是主观的,耗时的.
- 对精神分裂症需要客观和有效的评估方法.
- 负面症状包括表达 (EXP) 和动机和快乐 (MAP) 领域.
研究的目的:
- 评估一个使用生成AI (GenAI) 和机器学习 (ML) 进行精神分裂症负症状评估的自动化系统.
- 确定AI和ML在评估表达 (EXP) 和动机和快乐 (MAP) 领域的有效性.
- 为提供更客观,更有效的精神分裂症临床评估.
主要方法:
- 采用了一种半结构化的采访协议,基于临床评估对负面症状的采访.
- 通过高级精神病学家对精神分裂症患者进行了音频和视频录制的采访.
- 使用ML用于EXP的视觉/音频数据分析和GenAI用于MAP的转录分析.
主要成果:
- 基于ML的EXP评估显示了中度至实质性的可靠性 (ICC3,1=0.65,kappa=0.62).
- 基于GenAI的MAP评估显示出良好的可靠性 (ICC3,1=0.82,卡帕=0.77).
- 该系统与临床医生评级 (皮尔森r ≥0.54) 存在很强的相关性,并且错误率很低.
结论:
- GenAI和ML对于自动评估精神分裂症负面症状是有效的.
- 开发的系统提高了临床评估的一致性和效率.
- 这项技术有可能改善精神分裂症的诊断和管理.
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