  Characterization of a patient clinical phenotype is central to biomedical
informatics. ICD codes, assigned to inpatient encounters by coders, is
important for population health and cohort discovery when clinical information
is limited. While ICD codes are assigned to patients by professionals trained
and certified in coding there is substantial variability in coding. We present
a methodology that uses deep learning methods to model coder decision making
and that predicts ICD codes. Our approach predicts codes based on demographics,
lab results, and medications, as well as codes from previous encounters. We are
able to predict existing codes with high accuracy for all three of the test
cases we investigated: diabetes, acute renal failure, and chronic kidney
disease. We employed a panel of clinicians, in a blinded manner, to assess
ground truth and compared the predictions of coders, model and clinicians. When
disparities between the model prediction and coder assigned codes were
reviewed, our model outperformed coder assigned ICD codes.
