Generalized Bayesian Clustering with Regression for Unaligned Longitudinal Binary Data

2026-08-31

AI Summary

本文提出一种基于广义贝叶斯聚类和回归模型的方法，用于处理癫痫患者不规则、稀疏的纵向二值数据，通过轨迹相似性和回归损失结合实现有效聚类。

Abstract

We propose a generalized Bayesian clustering with regression model for unaligned longitudinal binary outcomes, motivated by seizure diary data from the Human Epilepsy Project. Seizure diaries are sparse, irregularly observed, and vary enormously across patients. A single fully-specified generative model tends to be either misspecified or computationally inefficient. We address the challenge by two strategies. We set up a regression by way of clustering as model-based clustering using a mixture model. For the latter, we take a generalized Bayesian perspective which replaces the full likelihood with a loss-based update using a generalized likelihood. We combine a trajectory similarity loss and a regression loss, so that clustering is informed by both trajectory similarity and the prediction of outcomes The trajectory similarity loss is constructed by representing each trajectory as an (empirical) distribution of subsequences, called reads, and then is defined based on the sliced Wasserstein distance between these empirical distributions. This loss allows alignment-free comparison of sequences that are irregularly observed or temporally misaligned, and it scales quasi-linearly in trajectory length. The regression loss is the negative log-likelihood of a probit regression. A prior on the cluster-specific parameters is defined by way of a Dirichlet process prior on the mixing measure.

Problem

• unaligned longitudinal binary data
• seizure diary data
• sparse and irregularly observed

Innovation

• Generalized Bayesian Clustering
• Trajectory Similarity Loss
• Sliced Wasserstein Distance
• Probit Regression