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

TL;DR

This paper introduces a novel approach that leverages medical text from electronic health records, enhanced by a knowledge graph and multiview graph convolutional networks, to accurately predict ICU readmission risk, outperforming previous methods.

Contribution

It presents a new text-based prediction method using knowledge graph-enhanced multiview graphs and graph convolutional networks for ICU readmission risk assessment.

Findings

Achieved state-of-the-art prediction accuracy

Effective use of medical text and external knowledge graphs

Demonstrated superiority over existing numerical feature-based methods

Abstract

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.