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心理AId:一个改进的DenseNet模型,以协助可扩展的精神病评估

Muxi Li^1,2, Farong Liu^3,4,5, Fei Du¹

¹State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, P R China.

|July 31, 2025

概括

一个新的AI模型,MentalAId,使用常规血液测试来准确检测精神病,为早期干预和监测提供了一个可扩展的解决方案,特别是在健康危机期间.

科学领域:

医疗保健中的人工智能
计算精神病学是一种计算精神病学.
生物标志物发现发现

背景情况:

随着COVID-19的爆发,人们越来越需要可扩展的心理健康评估.
传统的精神病评估仅限于大规模的纵向查.
由于评估的局限性,精神病的早期干预往往会被推迟.

研究的目的:

利用常规实验室数据开发一种人工智能工具,用于自动识别精神病.
评估使用标准血液测试用于精神病查的可行性.
创建一个具有成本效益和可扩展的解决方案,用于精神病评估.

主要方法:

开发了MentalAId,一个DenseNet模型,分析了49个血液学测试和人口统计数据.
在四个队伍中的28746个人的大型数据集上训练并验证了该模型.
包括精神病性住院患者,非精神病性住院患者,健康对照组和以前没有服用过药物的第一发精神病 (FEP) 患者.

主要成果:

在歧视性精神病中,MentalAId获得了93.3%的准确性和0.983的AUC.
该模型在现实条件下表现出强性,处理缺失和极端值.
准确检测未使用过药物的FEP患者 (91.9%准确率) 显示了早期识别的翻译潜力.

结论:

心理AId为精神病评估和监测提供了一种可访问,负担得起和可扩展的方法.
整合到医疗保健工作流程是无的,只需要标准的血液测试.
允许长期,全人口监测,在COVID-19等公共卫生危机期间至关重要.

关键词:

进行血液检查.深度学习是一种深度学习.第一个情节精神病的精神病.血液学特征血液学特征精神病的诊断是精神病的诊断.

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