arXiv：cs.LG· Barsha Halder, Jeffrey A. Thompson·· 4 小时前AI 评分34
DeepAJM：面向不规则采样数据的深度关联联合模型
DeepAJM: Deep Association Joint Model for Irregularly Sampled data
AI 导读
DeepAJM 是一种无需参数假设的深度联合模型，采用编码器-解码器（sequence-to-sequence）架构学习患者时变协变量的潜在结构，并通过可解释的关联结构将纵向过程与生存过程连接。在心血管疾病 EHR 队列、原发性胆汁性胆管炎（PBC2）数据集和模拟数据集上，该模型在 C-index、时间依赖 AUROC 和 AUPRC 上均取得最佳区分度。
正文
Abstract:Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric joint models, however, rely on fixed parametric assumptions, making them susceptible to bias under model misspecification and smaller sample sizes. We propose a deep joint model, DeepAJM, that does not require any parametric assumptions, while retaining a partially interpretable, per-longitudinal-outcome association structure. The joint model uses an encoder-decoder (sequence-to-sequence) architecture to learn the latent structure in patients' time-varying covariate trajectories. The model links the longitudinal processes to the survival processes through a learned interpretable association structure, in which each longitudinal output from the decoder gets remodulated by baseline covariates before it contributes to the risk scores from the survival head of the architecture. The model was evaluated on three datasets ( a cardiovascular-disease EHR cohort, a primary biliary cirrhosis (PBC2) dataset, and a simulated dataset) against a classical parametric joint model, TransformerJM, DA-LSTM and a Cox-based survival-only model. All models were assessed using C-index, integrated brier score (IBS), time-dependent AUROC, and time-dependent AUPRC. Our model achieved the best discrimination in terms of the C-index, time-dependent AUROC, and AUPRC across all datasets.
| Subjects: | Applications (stat.AP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.07388 [stat.AP] |
| (or arXiv:2610.07388v1 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07388 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Barsha Halder [view email]
[v1]
Mon, 5 Oct 2026 21:02:34 UTC (315 KB)
来源：arXiv：cs.LG · arxiv.org