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.

使用基于原型网络和标签注意力机制的 ProtoPatient 方法可准确预测临床文本中的诊断，并为医生提供可解释和有用的结果，定量和定性评估表明该模型优于现有基线模型，医学医生的进一步验证证明了该模型为临床决策支持提供了有价值的解释。

临床文本的原型网络对诊断预测的可解释性

This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text

Predicting diagnoses from Electronic Health Records (EHRs) is an important
medical application of multi-label learning. We propose a convolutional
residual model for multi-label classification from doctor notes in EHR data. A
given patient may have multiple diagnoses, and therefore multi-label learning
is required. We employ a Convolutional Neural Network (CNN) to encode plain
text into a fixed-length sentence embedding vector. Since diagnoses are
typically correlated, a deep residual network is employed on top of the CNN
encoder, to capture label (diagnosis) dependencies and incorporate information
directly from the encoded sentence vector. A real EHR dataset is considered,
and we compare the proposed model with several well-known baselines, to predict
diagnoses based on doctor notes. Experimental results demonstrate the
superiority of the proposed convolutional residual model.

提出了一种卷积残差模型，利用 CNN 对 EHR 数据中的医生笔记进行多标签分类预测，考虑到多个诊断之间的相关性，使用深度残差神经网络捕捉标签关系，并直接结合文本编码。实验结果表明，所提出的卷积残差模型优于现有的基线模型。