We present an operational component of a real-world patient triage system.
Given a specific patient presentation, the system is able to assess the level
of medical urgency and issue the most appropriate recommendation in terms of
best point of care and time to treat. We use an attention-based convolutional
neural network architecture trained on 600,000 doctor notes in German. We
compare two approaches, one that uses the full text of the medical notes and
one that uses only a selected list of medical entities extracted from the text.
These approaches achieve 79% and 66% precision, respectively, but on a
confidence threshold of 0.6, precision increases to 85% and 75%, respectively.
In addition, a method to detect warning symptoms is implemented to render the
classification task transparent from a medical perspective. The method is based
on the learning of attention scores and a method of automatic validation using
the same data.

本文介绍了一种实际上应用的病人分流系统的操作组件。通过对德文医生笔记的学习，使用了一种基于注意力机制的卷积神经网络模型，实现了对病人紧急性的评估和提供了最恰当的医疗建议以及时间。此外，一种检测警告症状的方法也被介绍。