  Unplanned intensive care unit (ICU) readmission rate is an important metric
for evaluating the quality of hospital care. Efficient and accurate prediction
of ICU readmission risk can not only help prevent patients from inappropriate
discharge and potential dangers, but also reduce associated costs of
healthcare. In this paper, we propose a new method that uses medical text of
Electronic Health Records (EHRs) for prediction, which provides an alternative
perspective to previous studies that heavily depend on numerical and
time-series features of patients. More specifically, we extract discharge
summaries of patients from their EHRs, and represent them with multiview graphs
enhanced by an external knowledge graph. Graph convolutional networks are then
used for representation learning. Experimental results prove the effectiveness
of our method, yielding state-of-the-art performance for this task.
