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Unplanned intensive care unit (ICU) readmission rate is an important metricfor evaluating the quality of hospital care. Efficient and accurate predictionof ICU readmission risk can not only help prevent patients from inappropriatedischarge and potential dangers, but also reduce associated costs ofhealthcare. In this paper, we propose a new method that uses medical text ofElectronic Health Records (EHRs) for prediction, which provides an alternativeperspective to previous studies that heavily depend on numerical andtime-series features of patients. More specifically, we extract dischargesummaries of patients from their EHRs, and represent them with multiview graphsenhanced by an external knowledge graph. Graph convolutional networks are thenused for representation learning. Experimental results prove the effectivenessof our method, yielding state-of-the-art performance for this task.

Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution