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机器学习用于诊断双相情感障碍的准确性:系统性审查和元分析

Yi Pan¹, Pushi Wang², Bowen Xue³

¹Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.

Frontiers in psychiatry

|February 12, 2025

概括

机器学习显示出对诊断双相情感障碍的前途,在将其与正常个人和抑郁症区分开来时达到高准确度. 未来的研究应该专注于多类分类,以改善临床应用.

科学领域:

心理健康心理健康
人工智能的人工智能
计算精神病学是一种计算精神病学.

背景情况:

诊断双相情感障碍是临床上具有挑战性和耗时的.
人工智能 (AI) 越来越多地用于心理健康的诊断模型.
机器学习 (ML) 模型正在开发用于双相情感障碍诊断,但它们的准确性仍有争议.

研究的目的:

系统地审查和评估机器学习在双相情感障碍诊断中的诊断价值.
评估ML模型在区分双相情感障碍和其他疾病方面的表现.

主要方法:

在PubMed,Embase,Cochrane和Web of Science进行了系统的文献搜索 (搜索结束于2023年4月1日).
通过QUADAS-2工具,评估了所包含的研究的质量.
一个双变的混合效应模型被用于元分析.

主要成果:

包括18项研究,包括3152名参与者 (1858名患有双相情感障碍),分析了28个ML模型.
ML模型显示高灵敏度 (0.88) 和特异性 (0.89) 来区分双相情感障碍与健康个体 (SROC=0.94).
为了区分双相情感障碍和抑郁症,ML模型实现了0.84的灵敏度和0.82的特异性 (SROC=0.89).

结论:

机器学习方法对于歧视和诊断双相情感障碍是有效的.
目前的ML应用主要是二元分类,限制了临床实用性.
未来的研究应该探索多类分类,以提高ML在双相情感障碍诊断中的临床适用性.

关键词:

双相情感障碍是双相情感障碍的一种疾病.抑郁抑郁症抑郁症抑郁症是一种机器学习是机器学习.预测模型是一个预测模型.系统性审查系统性审查

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