论文标题
ISEEU2：使用深度学习和自由文本医学笔记的视觉上可解释的ICU死亡率预测
ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes
论文作者
论文摘要
准确的死亡率预测允许重症监护病房（ICU）充分基准基准临床实践，并确定意外结果的患者。传统上，简单的统计模型已被用来评估患者死亡风险，多次以次优表现。另一方面，深度学习有望通过利用医学数据来帮助诊断和预测（包括死亡率预测）来积极影响临床实践。但是，随着在产生预测时是否有强大的深度学习模型参与以合理的医学知识为支持的问题，需要其他可解释性工具来促进信任并鼓励临床医生使用AI。在这项工作中，我们展示了一个深度学习模型，该模型在模仿III上训练，以使用原始护理笔记来预测死亡率，以及对单词重要性的视觉解释。我们的模型达到0.8629（+/- 0.0058）的ROC，与传统的SAPS-II分数优于传统的SAPS-II分数，并且与类似的深度学习方法相比，我们提供了增强的可解释性。
Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death risk, many times with sub-optimal performance. On the other hand deep learning holds promise to positively impact clinical practice by leveraging medical data to assist diagnosis and prediction, including mortality prediction. However, as the question of whether powerful Deep Learning models attend correlations backed by sound medical knowledge when generating predictions remains open, additional interpretability tools are needed to foster trust and encourage the use of AI by clinicians. In this work we show a Deep Learning model trained on MIMIC-III to predict mortality using raw nursing notes, together with visual explanations for word importance. Our model reaches a ROC of 0.8629 (+/-0.0058), outperforming the traditional SAPS-II score and providing enhanced interpretability when compared with similar Deep Learning approaches.
