Clinical notes contain a large amount of clinically valuable information that is ignored in many clinical decision support systems due to the difficulty that comes with mining that information. Recent work has found success leveraging deep learning models for the prediction of clinical outcomes using clinical notes. However, these models fail to provide clinically relevant and interpretable information that clinicians can utilize for informed clinical care. In this work, we augment a popular convolutional model with an attention mechanism and apply it to unstructured clinical notes for the prediction of ICU readmission and mortality. We find that the addition of the attention mechanism leads to competitive performance while allowing for the straightforward interpretation of predictions. We develop clear visualizations to present important spans of text for both individual predictions and high-risk cohorts. We then conduct a qualitative analysis and demonstrate that our model is consistently attending to clinically meaningful portions of the narrative for all of the outcomes that we explore.
翻译:临床记录包含大量临床有价值的信息,许多临床决策支持系统中都忽略了这些信息,而这些信息由于挖掘信息带来的困难而被忽视。最近的工作发现,利用深层学习模型成功地利用临床记录预测临床结果,但这些模型未能提供临床相关和可解释的信息,临床医生可以用于知情的临床护理。在这项工作中,我们利用关注机制加强流行的演进模型,并将其应用于无结构的临床说明,以预测伊斯兰法院联盟的再接收和死亡率。我们发现,增加关注机制导致竞争性性能,同时允许对预测进行直截了当的解释。我们开发清晰的可视化工具,为个别预测和高风险组群提供重要的文本范围。我们随后进行定性分析,并表明我们的模型正在持续关注具有临床意义的叙述部分,以了解我们探索的所有结果。