自动RADP:一个可解释的深度学习框架,用电子健康记录来预测阿尔茨海默病和相关痴呆症的快速进展
medRxiv : the preprint server for health sciences
|April 29, 2025
概括
本研究介绍了AutoRADP,这是一个AI框架,使用电子健康记录预测阿尔茨海默病 (AD) 和相关痴呆症 (ADRD) 的快速进展. AutoRADP为更好的临床决策和个性化的患者护理提供了准确,可解释的见解.
科学领域:
- 人工智能在医学中的应用
- 神经退行性疾病研究研究
- 生物医学信息学 生物医学信息学
背景情况:
- 阿尔茨海默病 (AD) 和与AD相关的痴呆症 (ADRD) 呈现出不同的进展率,使早期干预复杂化.
- 电子健康记录 (EHR) 为开发疾病轨迹的预测模型提供了丰富的数据.
研究的目的:
- 开发一个可解释的机器学习框架,AutoRADP,用于预测AD/ADRD的快速进展.
- 利用结构化和非结构化EHR数据来提高预测准确度.
主要方法:
- 利用基于自动编码器的框架 (AutoRADP) 结合基于规则的NLP来从临床笔记中进行认知评估.
- 采用特征选择和混合抽样策略来解决数据不平衡.
- 综合的夏普利添加式解释 (SHAP) 用于模型的解释性.
主要成果:
- 与现有方法相比,AutoRADP在预测AD/ADRD快速进展方面表现优越.
- 通过可解释的SHAP值,确定了促进疾病快速进展的关键因素.
- 从非结构化的临床笔记中成功提取了关键的认知数据.
结论:
- 通过能够准确和可解释地预测AD/ADRD的快速进展,AutoRADP促进了精准医学的发展.
- 该框架支持改善临床决策,并为AD/ADRD患者提供个性化干预.
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