NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

• 类型：arxiv
• 标识：2609.09140
• 链接：https://arxiv.org/abs/2609.09140
• 主分类：multimodal
• 形态：method
• TLDR：The digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploiting these data to represent and predict patient state trajectories remains a critical challenge. Current AI models often struggle to capture the complex, irregular temporal dynamics and inherent stochasticity of real-world multimodal patient data. Existing AI approaches for modeling longitudinal patient records are predominantly discriminative, limited to a few modalities, constrained by closed categorical vocabularies, treating time as a monotonic inductive bias, o
• 待LLM分类：否
• 标题中文：NOAH：学习完整患者旅程的纵向多模态时序感知表示与预测模型
• TLDR中文：医疗数字化产生了海量、纵向、多模态的终身患者记录，但充分利用这些数据以表征和预测患者状态轨迹仍是关键挑战。现有 AI 模型难以捕捉真实世界多模态患者数据中复杂且不规则的时间动态与固有随机性。既有纵向患者记录建模方法以判别式为主，模态支持有限，受限于封闭类别词表，将时间视为单调归纳偏置……
• 来源文件：
• /inbox/tom/_candidates/2026-09-10-agent-rag-longcontext-candidates.json