Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning

Chmiel, F. P., Azor, M., Borca, F., Boniface, M. J., Burns, D. K., Zlatev, Z. D., White, N. M., Daniels, T. W.V. and Kiuber, M. (2020) Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning. medRxiv. (doi:10.1101/2020.12.02.20239194).

Abstract

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance can help reduce the number of patients with missed or undertreated illness or injury, and could support appropriate discharges with focused interventions. In this manuscript we present a retrospective, single-centre study where we create and evaluate a machine-learnt classifier trained to identify patients at risk of reattendance within 72 hours of discharge from an emergency department. On a patient hold-out test set, our highest performing classifier obtained an AUROC of 0.748 and an average precision of 0.250; demonstrating that machine-learning algorithms can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. In parallel to our predictive model we train an explanation model, capable of explaining predictions at an attendance level, which can be used to help inform the design of interventional strategies.

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Contributors

Author: M. J. Boniface

Author: D. K. Burns

Author: N. M. White

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