DENSE：跨医院就诊的异构临床记录时间建模与纵向进展记录生成
DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits
AI总结：
DENSE系统通过细粒度记录分类、时间对齐机制和临床信息检索策略，从跨就诊的异构临床记录中生成时间感知的纵向进展记录，在MIMIC-III上取得1.089的时间对齐比率，优于原始记录连续性。
摘要：
进展记录是电子健康记录（EHR）中最具临床意义的产物之一，为患者病情演变、治疗和护理决策提供了基于时间的洞察。尽管其重要性显著，但在大规模EHR数据集中，进展记录严重不足。例如，在广泛使用的重症监护医学信息集市III（MIMIC-III）数据集中，仅有约8.56%的医院就诊包含进展记录，导致纵向患者叙事存在空白。相比之下，该数据集包含多种其他类型的记录，每种记录捕捉护理的不同方面。我们提出DENSE（Documenting Evolving Progress Notes from Scattered Evidence，从分散证据中记录演进的进展记录），该系统旨在与临床文档工作流程对齐，模拟医生在撰写进展记录时参考既往就诊记录的方式。该系统引入了细粒度的记录分类和时间对齐机制，将跨就诊的异构记录组织为结构化的、按时间顺序排列的输入。其核心在于，DENSE利用一种基于临床信息的检索策略，从当前和既往就诊中识别时间和语义上相关的内容。检索到的证据被用于提示大语言模型（LLM）生成临床连贯且具有时间感知能力的进展记录。我们在一个经过筛选的、具有多次就诊且进展记录文档完整的患者队列上对DENSE进行了评估。生成的记录展现出很强的纵向保真度，时间对齐比率达到1.089，超过了原始记录中观察到的连续性。通过恢复碎片化文档之间的叙事连贯性，我们的系统支持改进下游任务，如摘要生成、预测建模和临床决策支持，为现实世界医疗环境中的LLM驱动记录合成提供了一种可扩展的解决方案。
英文摘要：
Progress notes are among the most clinically meaningful artifacts in an Electronic Health Record (EHR), offering temporally grounded insights into a patient's evolving condition, treatments, and care decisions. Despite their importance, they are severely underrepresented in large-scale EHR datasets. For instance, in the widely used Medical Information Mart for Intensive Care III (MIMIC-III) dataset, only about $8.56\%$ of hospital visits include progress notes, leaving gaps in longitudinal patient narratives. In contrast, the dataset contains a diverse array of other note types, each capturing different aspects of care. We present DENSE (Documenting Evolving Progress Notes from Scattered Evidence), a system designed to align with clinical documentation workflows by simulating how physicians reference past encounters while drafting progress notes. The system introduces a fine-grained note categorization and a temporal alignment mechanism that organizes heterogeneous notes across visits into structured, chronological inputs. At its core, DENSE leverages a clinically informed retrieval strategy to identify temporally and semantically relevant content from both current and prior visits. This retrieved evidence is used to prompt a large language model (LLM) to generate clinically coherent and temporally aware progress notes. We evaluate DENSE on a curated cohort of patients with multiple visits and complete progress note documentation. The generated notes demonstrate strong longitudinal fidelity, achieving a temporal alignment ratio of $1.089$, surpassing the continuity observed in original notes. By restoring narrative coherence across fragmented documentation, our system supports improved downstream tasks such as summarization, predictive modeling, and clinical decision support, offering a scalable solution for LLM-driven note synthesis in real-world healthcare settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
