基于引导式多向注意的临床笔记问题列表动态抽取
Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
摘要：
问题列表旨在为临床医生提供患者医疗问题的相关摘要，并嵌入在许多电子健康记录系统中。尽管它们很重要，但问题列表中经常充斥着已解决或当前不相关的条件。在这项工作中，我们开发了一个新的端到端框架，首先从临床笔记中提取诊断和程序信息，然后使用提取的医疗问题来预测患者的预后。该框架比该领域内使用的现有模型性能更高，更可解释，对于ICU出院后发生的反弹再入院和院内死亡率，AU-ROC分别为0.710和0.869。我们确定了再入院和死亡率结果的风险因素，并证明我们的框架可以用来开发动态的问题列表，目前的临床问题及其定量的重要性。我们与医学专家进行了一项定性用户研究，并证明他们认为我们的框架产生的列表有利，并发现它们是一个更有效的临床决策支持工具，而不是一个强大的基线。
英文摘要：
Problem lists are intended to provide clinicians with a relevant summary of patient medical issues and are embedded in many electronic health record systems. Despite their importance, problem lists are often cluttered with resolved or currently irrelevant conditions. In this work, we develop a novel end-to-end framework that first extracts diagnosis and procedure information from clinical notes and subsequently uses the extracted medical problems to predict patient outcomes. This framework is both more performant and more interpretable than existing models used within the domain, achieving an AU-ROC of 0.710 for bounceback readmission and 0.869 for in-hospital mortality occurring after ICU discharge. We identify risk factors for both readmission and mortality outcomes and demonstrate that our framework can be used to develop dynamic problem lists that present clinical problems along with their quantitative importance. We conduct a qualitative user study with medical experts and demonstrate that they view the lists produced by our framework favorably and find them to be a more effective clinical decision support tool than a strong baseline.
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。
