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Clinical notes are text documents that are created by clinicians for eachpatient encounter. They are typically accompanied by medical codes, whichdescribe the diagnosis and treatment. Annotating these codes is labor intensiveand error prone; furthermore, the connection between the codes and the text isnot annotated, obscuring the reasons and details behind specific diagnoses andtreatments. We present an attentional convolutional network that predictsmedical codes from clinical text. Our method aggregates information across thedocument using a convolutional neural network, and uses an attention mechanismto select the most relevant segments for each of the thousands of possiblecodes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of0.54, which are both better than the prior state of the art. Furthermore,through an interpretability evaluation by a physician, we show that theattention mechanism identifies meaningful explanations for each code assignment

Explainable Prediction of Medical Codes from Clinical Text