基于辅助替代标签的潜在疾病进展动态分类
Dynamic Classification of Latent Disease Progression with Auxiliary Surrogate Labels
AI总结：
针对真实疾病状态未知下的疾病进展预测难题，提出结合隐马尔可夫生成模型与时变判别分类模型的方法，开发自适应估计算法，在模拟和NACC数据集上均实现预测精度提升。
摘要：
当由于诊断能力有限或诊断成本高昂导致真实疾病状态未知时，基于患者不断变化的健康信息进行疾病进展预测极具挑战性。例如，缺乏金标准的神经系统诊断阻碍了阿尔茨海默病（AD）与相关病症的区分，这类病症包括路易体痴呆（LBD）在内的AD相关痴呆（ADRDs）。结合时间依赖的替代标签与健康标志物或可提升疾病预测效果。但现有文献采用纯生成式方法对反映潜在状态的信息性替代标签和观测变量建模，限制了对未来状态的预测能力。我们提出将作为生成模型的传统隐马尔可夫模型与时变判别分类模型相结合，以同时处理可能存在误设的替代标签，并纳入疾病进展的重要标志物。我们开发了一种带主观标签的自适应前向-后向算法用于估计，并利用改进的后验算法和Viterbi算法，仅基于客观标志物预测未来状态进展或新患者的病情进展。重要的是，这种适配无需对纵向标志物的边际分布建模，而这是传统算法的必要条件。我们建立了该方法的渐近性质，并通过模拟研究证明了其在有限样本下的显著提升。对美国国家阿尔茨海默病协调中心（NACC）神经病理学数据集的分析显示，该方法在区分LBD与AD的准确性上有大幅提升。
英文摘要：
Disease progression prediction based on patients' evolving health information is challenging when true disease states are unknown due to diagnostic capabilities or high costs. For example, the absence of gold-standard neurological diagnoses hinders distinguishing Alzheimer's disease (AD) from related conditions such as AD-related dementias (ADRDs), including Lewy body dementia (LBD). Combining temporally dependent surrogate labels and health markers may improve disease prediction. However, existing literature models informative surrogate labels and observed variables that reflect the underlying states using purely generative approaches, limiting the ability to predict future states. We propose integrating the conventional hidden Markov model as a generative model with a time-varying discriminative classification model to simultaneously handle potentially misspecified surrogate labels and incorporate important markers of disease progression. We develop an adaptive forward-backward algorithm with subjective labels for estimation, and utilize the modified posterior and Viterbi algorithms to predict the progression of future states or new patients based on objective markers only. Importantly, the adaptation eliminates the need to model the marginal distribution of longitudinal markers, a requirement in traditional algorithms. Asymptotic properties are established, and significant improvement with finite samples is demonstrated via simulation studies. Analysis of the neuropathological dataset of the National Alzheimer's Coordinating Center (NACC) shows much improved accuracy in distinguishing LBD from AD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
