论文标题
关于临床笔记在多模式学习中对于EHR数据的重要性
On the Importance of Clinical Notes in Multi-modal Learning for EHR Data
论文作者
论文摘要
了解深度学习模型行为对于接受医学界的基于机器学习的决策支持系统至关重要。先前的研究表明，使用电子健康记录（EHR）数据共同使用临床注释改善了重症监护病房（ICU）中患者监测的预测性能。在这项工作中，我们探讨了这些改进的根本原因。在依靠基于注意力的基本模型允许可解释性的同时，我们首先确认在结合EHR数据和临床注释时，性能会显着改善EHR数据模型。然后，我们提供了一个分析，显示改进几乎完全来自包含有关患者状态而不是临床医生注释的更广泛背景的注释。我们认为，这样的发现突出了EHR数据的深度学习模型，而不是通过对选择进行建模，更受部分描述性数据的限制，从而激发了以数据为中心的方法。
Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community. Previous research has shown that jointly using clinical notes with electronic health record (EHR) data improved predictive performance for patient monitoring in the intensive care unit (ICU). In this work, we explore the underlying reasons for these improvements. While relying on a basic attention-based model to allow for interpretability, we first confirm that performance significantly improves over state-of-the-art EHR data models when combining EHR data and clinical notes. We then provide an analysis showing improvements arise almost exclusively from a subset of notes containing broader context on patient state rather than clinician notes. We believe such findings highlight deep learning models for EHR data to be more limited by partially-descriptive data than by modeling choice, motivating a more data-centric approach in the field.
