从人群规范到个性化轨迹：用于认知衰退的可解释贝叶斯预测
From population norms to personalized trajectories: interpretable Bayesian forecasting for cognitive decline
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中文总结 AI 辅助
本研究开发了可解释框架PRISM，基于常规数据通过贝叶斯推理预测认知衰退，在两项大型队列中表现优于基线模型，可更早识别个体认知衰退，支持及时评估。
中文摘要 AI 辅助
个体间认知衰退的时间和速率存在显著差异，这限制了固定人群水平阈值判断新观察值是否代表个体有意义变化的能力。我们开发了PRISM（Personalized Risk Inference via Sequential Monitoring，即通过序列监测实现个性化风险推理），这是一个用于认知衰退个性化纵向预测的可解释框架。PRISM使用可解释增强机（Explainable Boosting Machine）从常规收集的人口统计学、健康和功能变量中估计个性化认知基线，随后随着认知评分的积累，通过带有时间衰减的贝叶斯推理更新该预期。衰退是相对于年龄调整的个人锚点进行评估的，不确定性通过后验概率量化。我们在来自健康与退休研究（Health and Retirement Study）的30664名成年人中评估了PRISM，并在来自阿尔茨海默病神经成像倡议（Alzheimer's Disease Neuroimaging Initiative）的1866名参与者中对其进行了外部验证。将PRISM的预测性能与人口统计学规范和累积平均基线进行比较，并将其辨别力与线性混合效应模型进行比较。PRISM在健康与退休研究中识别出31%的持续衰退者在研究定义的认知恶化之前出现新的衰退，在阿尔茨海默病神经成像倡议中这一比例为41%，中位领先时间分别为6年和2年。到恶化发生时，分别有68%和56%的人已被识别。PRISM还实现了比人口统计学规范和累积平均基线更低的预测误差，并且比线性混合效应模型更好地区分了恶化与稳定轨迹，尤其是在随访早期。PRISM使用常规收集的数据，相对于每个个体的预期轨迹，能够更早、可解释且感知不确定性地检测认知衰退，当个人纵向历史有限时，它可能支持更密切的监测和及时评估。
英文摘要
The timing and rate of cognitive decline vary substantially between individuals, limiting the ability of fixed population-level thresholds to determine whether a new observation represents meaningful change for an individual. We developed Personalized Risk Inference via Sequential Monitoring (PRISM), an interpretable framework for individualized longitudinal forecasting of cognitive decline. PRISM estimates a personalized cognitive baseline from routinely collected demographic, health, and functional variables using an Explainable Boosting Machine, then updates this expectation through Bayesian inference with temporal decay as cognitive scores accrue. Decline is evaluated relative to an age-adjusted personal anchor, with uncertainty quantified through posterior probabilities. We evaluated PRISM in 30,664 adults from the Health and Retirement Study and externally validated it in 1,866 Alzheimer's Disease Neuroimaging Initiative participants. Forecasting performance was compared with demographic-norm and cumulative-average baselines, and discrimination with a linear mixed-effects model. PRISM identified emerging decline before study-defined cognitive worsening in 31% of sustained decliners in the Health and Retirement Study and 41% in the Alzheimer's Disease Neuroimaging Initiative, with median lead times of 6 and 2 years, respectively. By the time of worsening, 68% and 56% had been identified. PRISM also achieved lower forecasting error than demographic-norm and cumulative-average baselines and distinguished worsening from stable trajectories better than a linear mixed-effects model, particularly early in follow-up. PRISM enables earlier, interpretable, uncertainty-aware detection of cognitive decline relative to each individual's expected trajectory using routinely collected data. It may support closer monitoring and timely assessment when personal longitudinal history is limited.
