基于机器学习算法的估计模型,使用蒙哥马利-阿斯伯格抑郁症评级尺度评估抑郁症的严重程度
Masanori Shimamoto1, Kanako Ishizuka2, Kento Ohtani3
1Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Neuropsychopharmacology reports
|December 20, 2023
概括
人工智能-MADRS平均估计模型在评估抑郁症严重程度方面显示出良好的可靠性,与训练有素的精神病医生相比. 这种人工智能技术有可能使心理健康评估现代化.
科学领域:
- 精神病学和人工智能的人工智能
- 计算精神病学是一种计算精神病学.
- 心理健康技术 心理健康技术
背景情况:
- 抑郁症的评估传统上依赖于训练有素的专业人士使用评级表.
- 确保与这些尺度一致的数据收集是一个重大挑战.
- 人工智能驱动的工具为客观和可靠的抑郁症严重程度评估提供了潜在的解决方案.
研究的目的:
- 评估AI-MADRS (人工智能-蒙特哥马利-阿斯伯格抑郁症评分表) 系统的评分器和估计系统可靠性.
- 将AI-MADRS估计系统的性能与受过训练的精神病学家的共识评估进行比较.
- 评估两个AI-MADRS模型的可靠性:最大估计和平均估计.
主要方法:
- 患者通过口头提示回应AI-MADRS结构化采访问题.
- 严重性得分是由两个AI-MADRS模型 (最大和平均估计) 生成的.
- 人工智能生成的分数与训练有素的精神病学家的共识评估的分数进行了比较.
主要成果:
- 分析包括30名患者的51次采访.
- 平均估计模型实现了皮尔森与精神病学家得分的0.86的相关性.
- 平均估计模型显示,随着精神病学家的评估,ANOVA ICC可靠性为0.75.
结论:
- 人工智能-MADRS平均估计模型在训练有素的精神病医生中显示出相当可接受的可靠性.
- 预计随着更大的数据集和精细的AI-MADRS访谈,将进一步改进.
- 人工智能技术在现代化和可能彻底改变抑郁症评估方面表现有前途.
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