论文标题
该患者看起来像那个患者：可解释的诊断预测的典型网络
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text
论文作者
论文摘要
从临床文本中使用深层神经模型进行诊断预测，已显示出令人鼓舞的结果。但是，在临床实践中，这种模型不仅必须是准确的，而且可以为医生提供可解释和有用的结果。我们介绍了Protopatient，这是一种基于原型网络的新颖方法，并具有这两种能力的标签。原始者根据文本的一部分与原型患者相似，做出预测 - 提供医生理解的理由。我们在两个公开可用的临床数据集上评估了该模型，并表明它的表现优于现有基准。与医生进行的定量和定性评估进一步表明，该模型为临床决策支持提供了宝贵的解释。
The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients - providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines. Quantitative and qualitative evaluations with medical doctors further demonstrate that the model provides valuable explanations for clinical decision support.
