论文标题
使用时间卷积网络的败血症预测
Sepsis Prediction with Temporal Convolutional Networks
论文作者
论文摘要
我们设计并实施了时间卷积网络模型，以预测败血症的发作。根据对重症监护病房的患者的回顾性分析，我们的模型对从模仿III数据库中提取的数据进行了培训，这些患者在入院时未属于败血症的定义。通过几种机器学习模型的基准测试，我们的模型在这项二进制分类任务上表现出了优越，展示了卷积网络对时间模式的预测能力，还显示了更长的回顾时间对败血症预测的重大影响。
We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective analysis of patients admitted to intensive care unit who did not fall under the definition of sepsis at the time of admission. Benchmarked with several machine learning models, our model is superior on this binary classification task, demonstrates the prediction power of convolutional networks for temporal patterns, also shows the significant impact of having longer look back time on sepsis prediction.
