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.
翻译:深入了解学习模式行为对于在医疗界接受基于学习的机器决策支持系统至关重要。 先前的研究显示,联合使用带有电子健康记录(EHR)数据的临床笔记可以提高特护单位病人监测的预测性能。 在这项工作中,我们探讨了这些改进的根本原因。我们依靠基于基本关注的模型来进行解释,但我们首先确认,在将EHR数据和临床笔记合并时,与最新的EHR数据模型相比,业绩有了显著改善。我们然后提供了一项分析,表明进展几乎完全来自包含病人状况更广泛背景的笔记,而不是临床笔记。我们认为,这些调查结果突出表明,EHR数据的深层次学习模式比模拟选择更受部分描述性数据的限制,从而促使在实地采取更以数据为中心的方法。