利用病历文本提升出院后死亡率预测准确率，效果显著
Enhance the after-discharge mortality rate prediction via learning from the medical notes
- 设计带池化机制的深度神经网络，聚焦关键病历信息
- 模型在15至365天预测中AUC-ROC提升2%至14%
- 可发现病历关键词与患者病情严重程度的关联
随着电子健康记录（EHR）数据的增长，越来越多研究者尝试从病历文本中学习。这些非结构化文本数据质量低，常杂乱重复。我们通过出院后死亡率预测任务证明病历数据具有信息价值：使用病历信息的模型AUC-ROC普遍比不使用高出0.1。为此，提出一种带池化机制的深度神经网络（DNN），显著优于传统树模型。实验表明，该方法在15天、30天、60天及365天的死亡率预测中，AUC-ROC提升2%至14%。此外，模型能揭示病历关键词与患者病情严重程度之间的关联，结果虽具启发性但与既有研究一致。
原文摘要 · Abstract (English)
With the increase of the Electronic Health Records (EHR) data, more and more researchers are developing machine learning models to learn from the medical notes. These unstructured text data pose significant challenges on the learning process as the quality of data is low. These data are often messy, repetitive and redundant. We have shown these notes data to be informative by conducting the after-discharge mortality rate prediction task. The AUC-ROC for models using the medical note information is generally 0.1 higher than those without the medical notes. Furthermore, we propose the Deep Neural Network(DNN) model with 'pooling' mechanism to enhance the mortality prediction. Based on the experimental results, we demonstrate that the proposed model outperforms the traditional machine learning models like the tree-based models. The proposed method learns from the most informative medical notes and improves the prediction accuracy significantly. The AUC-ROC for the proposed model is 2% to 14% higher than the traditional ones in 15-days, 30-days, 60-days, 365-days after-discharge mortality prediction tasks. Moreover, we can discover some interesting knowledge through the traditional and proposed models. These knowledge are inspiring but also consistent with the previous findings. The models are able to reveal the relationships between the informative keywords and documents from the medical notes and the severity of the patients.
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