Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment. Typically, practitioners seek to predict the distribution of a discrete disease score, conditional on an individual's current MRI volume and their historical disease trajectory. Classical statistical regression models and single-task neural networks are not well-suited for this purpose because fitting separate models is infeasible (since each individual typically has few observations), while ignoring individual-level correlation leads to poor generalization. Meta-learning, in contrast, provides a natural avenue to dynamically predict distributions without retraining and model nonlinear relationships between the outcome and covariates. Motivated by this, we propose a Bayesian meta-learner that is trained on multiple individuals but tailors the predictive disease score distribution to each individual's historical data. Our model predicts on unseen individuals without retraining, scales linearly with the number of historical observations, and is guaranteed to be less overconfident when predicting long-term disease scores compared to its deterministic counterpart. On real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, our model achieves performance competitive with both single-task models and deterministic meta-learners, while substantially improving performance when predicting long-term disease progression.

翻译：预测阿尔茨海默病患者将经历轻度还是重度疾病进展，对于个性化治疗至关重要。临床实践通常需要根据个体当前的MRI脑容量数据和历史病程轨迹，预测离散型疾病评分的分布。经典统计回归模型和单任务神经网络在此任务中存在局限：为每个个体独立拟合模型不可行（因个体观测数据通常较少），而忽略个体间相关性则会导致泛化能力不足。相比之下，元学习为无需重新训练即可动态预测分布、同时建模结局变量与协变量间非线性关系提供了自然路径。受此启发，我们提出一种贝叶斯元学习模型，该模型基于多个个体的训练数据，但能针对每个个体的历史数据定制疾病评分预测分布。我们的模型无需重新训练即可预测未见个体，预测复杂度与历史观测数量呈线性关系，且与确定性对应方法相比，在预测长期疾病评分时能有效避免过度自信。基于阿尔茨海默病神经影像学倡议（ADNI）数据库的真实世界数据，我们的模型在性能上与单任务模型和确定性元学习模型相当，且在预测长期疾病进展时表现出显著更优的性能。