English(EN) DeepAJM: Deep Association Joint Model for Irregularly Sampled data

DeepAJM：新型深度学习模型提升生存结果预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 21:02

研究人员开发了DeepAJM，这是一种新颖的深度学习联合模型，旨在通过分析纵向数据轨迹来预测生存结果。与依赖固定假设的传统参数模型不同，DeepAJM使用编码器-解码器架构来学习潜在结构，而无需参数假设。这种方法允许对纵向过程和生存预测之间的关联结构进行部分解释。在心血管疾病EHR、PBC2和模拟数据集上的评估表明，DeepAJM在C-index、AUROC和AUPRC等判别指标上优于经典的参数联合模型、TransformerJM、DA-LSTM和仅基于Cox的生存模型。 AI

影响这种新的深度学习模型提高了从纵向数据预测生存结果的准确性，有望推动医学研究和临床决策。

排序理由该集群描述了一篇研究论文中提出的新型深度学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-05 21:02

DeepAJM：用于不规则采样数据的深度关联联合模型

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 suscept…

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DeepAJM：用于不规则采样数据的深度关联联合模型

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