  Predicting which patients are more likely to be readmitted to a hospital
within 30 days after discharge is a valuable piece of information in clinical
decision-making. Building a successful readmission risk classifier based on the
content of Electronic Health Records (EHRs) has proved, however, to be a
challenging task. Previously explored features include mainly structured
information, such as sociodemographic data, comorbidity codes and physiological
variables. In this paper we assess incorporating additional clinically
interpretable NLP-based features such as topic extraction and clinical
sentiment analysis to predict early readmission risk in psychiatry patients.
