用智能筛选减少昂贵检测，精准预测老年痴呆进展
An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression
- 根据信息价值动态决定是否做昂贵检测，降低20%测试需求
- 准确率达AUROC 0.929，接近全数据模型的0.915
- 可解释决策过程，适合临床路径优化与资源受限场景
准确预测轻度认知障碍（MCI）向阿尔茨海默病（AD）转化对实现个性化治疗至关重要。尽管常规认知测试和临床数据易获取，但其预测能力远低于正电子发射断层扫描（PET）和脑脊液（CSF）生物标志物分析，而后者因成本过高难以普遍应用。为解决这一成本-精度矛盾，我们设计了一个两阶段机器学习分诊框架，基于预测的“信息价值”选择性获取高成本特征。在阿尔茨海默病神经影像计划（ADNI）数据上应用该框架，将高级检测需求降低20%，同时达到测试AUROC 0.929，与使用全部基础和高级特征的模型（AUROC=0.915，p=0.1010）相当。我们还展示了可解释性分析示例，说明如何解释分诊决策。本研究提供了一个可解释、数据驱动的框架，优化了阿尔茨海默病诊断路径，在准确性与成本间取得平衡，推动早期可靠预测在真实世界中的普及。未来工作应考虑更多类别的高级特征并开展更大规模验证。
原文摘要 · Abstract (English)
Accurate predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) can enable effective personalized therapy. While cognitive tests and clinical data are routinely collected, they lack the predictive power of PET scans and CSF biomarker analysis, which are prohibitively expensive to obtain for every patient. To address this cost-accuracy dilemma, we design a two-stage machine learning framework that selectively obtains advanced, costly features based on their predicted "value of information". We apply our framework to predict AD progression for MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our framework reduces the need for advanced testing by 20% while achieving a test AUROC of 0.929, comparable to the model that uses both basic and advanced features (AUROC=0.915, p=0.1010). We also provide an example interpretability analysis showing how one may explain the triage decision. Our work presents an interpretable, data-driven framework that optimizes AD diagnostic pathways and balances accuracy with cost, representing a step towards making early, reliable AD prediction more accessible in real-world practice. Future work should consider multiple categories of advanced features and larger-scale validation.
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