  Heart failure hospitalization is a severe burden on healthcare. How to
predict and therefore prevent readmission has been a significant challenge in
outcomes research. To address this, we propose a deep learning approach to
predict readmission from clinical notes. Unlike conventional methods that use
structured data for prediction, we leverage the unstructured clinical notes to
train deep learning models based on convolutional neural networks (CNN). We
then use the trained models to classify and predict potentially high-risk
admissions/patients. For evaluation, we trained CNNs using the discharge
summary notes in the MIMIC III database. We also trained regular machine
learning models based on random forest using the same datasets. The result
shows that deep learning models outperform the regular models in prediction
tasks. CNN method achieves a F1 score of 0.756 in general readmission
prediction and 0.733 in 30-day readmission prediction, while random forest only
achieves a F1 score of 0.674 and 0.656 respectively. We also propose a
chi-square test based method to interpret key features associated with deep
learning predicted readmissions. It reveals clinical insights about readmission
embedded in the clinical notes. Collectively, our method can make the human
evaluation process more efficient and potentially facilitate the reduction of
readmission rates.
