Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution
This addresses the issue of inefficient and costly ICU readmissions for hospitals and patients, though it is incremental as it builds on existing graph-based methods with a new text-based perspective.
The paper tackled the problem of predicting unplanned ICU readmission risk by using medical text from EHRs, achieving state-of-the-art performance with a method based on multiview graphs enhanced by a knowledge graph and graph convolutional networks.
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.