  Clinical notes are text documents that are created by clinicians for each
patient encounter. They are typically accompanied by medical codes, which
describe the diagnosis and treatment. Annotating these codes is labor intensive
and error prone; furthermore, the connection between the codes and the text is
not annotated, obscuring the reasons and details behind specific diagnoses and
treatments. We present an attentional convolutional network that predicts
medical codes from clinical text. Our method aggregates information across the
document using a convolutional neural network, and uses an attention mechanism
to select the most relevant segments for each of the thousands of possible
codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of
0.54, which are both better than the prior state of the art. Furthermore,
through an interpretability evaluation by a physician, we show that the
attention mechanism identifies meaningful explanations for each code assignment
