  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.
