一个可解释的贝叶斯框架,用于预测阿尔茨海默病的不确定性量化.
Ratnadeep Das1, Atri Chatterjee2, Sitikantha Roy3
1Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, Delhi 110016, India.
Neuroscience
|October 17, 2025
概括
一个新的贝叶斯编码解码器GRU (BEND-GRU) 框架使用认知分数准确预测阿尔茨海默病的进展. 该工具量化了预测的不确定性,并提供了推理,帮助阿尔茨海默病管理的临床决策.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 阿尔茨海默氏病 (AD) 的进展是可变的,需要用于临床管理的准确预测工具.
- 当前的预测模型往往缺乏不确定性量化和解释性,限制了它们的临床实用性.
- 利用多样化的数据模式对于改善AD进展预测至关重要.
研究的目的:
- 开发一种新的贝叶斯编码解码器GRU (BEND-GRU) 框架,用于预测阿尔茨海默病评估量表 (ADAS-13) 和临床痴呆症评分 - 盒子总和 (CDR-SB) 评分.
- 通过结合模型可变性和数据噪声来量化预测不确定性.
- 使用集成梯度方法提供可解释的预测.
主要方法:
- 利用了来自阿尔茨海默病神经成像计划 (ADNI) 数据集的基线和第一年数据.
- 开发了一个贝叶斯编码解码器GRU (BEND-GRU) 模型用于得分预测.
- 采用集成梯度用于模型解释性和不确定性量化.
主要成果:
- BEND-GRU模型在预测第二年和第三年ADAS-13和CDR-SB分数方面取得了很高的准确性.
- 2 年 ADAS-13 预测:MAE 2.98,R2 0.83; 3 年 ADAS-13 预测:MAE 3.60,R2 0.80.
- 第二年CDR-SB预测:MAE 0.69,R2 0.76;第三年CDR-SB预测:MAE 0.95,R2 0.72. 这是一个很好的预测.
- 废弃性研究证实了该框架的有效性,即使数据模式有限 (例如,人口统计,认知得分).
结论:
- BEND-GRU框架提供了对阿尔茨海默病进展的准确和可解释的预测.
- 该模型量化了不确定性,为临床医生提供了预测的置信区间.
- 确定的主要预测因素包括迷你心理状态考试 (MMSE) 评分和延迟召回总分,突出了框架在资源有限的环境中的实用性.
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