English(EN) Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture

基于 Mamba 的模型改进了从电子健康记录数据进行的患者子类型划分

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-26 21:45

研究人员开发了一种新的自监督模型，该模型利用基于 Mamba 的架构来改进从纵向电子健康记录 (EHR) 中进行的患者子类型划分。这种方法解决了 EHR 数据复杂性和不规则性带来的挑战。该模型学习有效的表示，从而增强了患者子类型划分和分类能力，在广泛的实验中表现优于现有的基线模型。 AI

影响为处理复杂的纵向健康数据提供了一种新颖的方法，有可能提高诊断准确性和个性化治疗策略。

排序理由该集群包含一篇学术论文，详细介绍了新的模型架构及其在特定问题领域的应用。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Md Mozaharul Mottalib, Rahmatollah Beheshti · 2026-06-30 04:00

Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture

arXiv:2606.28623v1 Announce Type: cross Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challen…
arXiv stat.ML TIER_1 English(EN) · Rahmatollah Beheshti · 2026-06-26 21:45

Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture

Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complex…