  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.
