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

Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning
Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning
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.
Chmiel, F. P.
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Azor, M.
f857a719-496d-4b79-b28a-17af196a95d8
Borca, F.
31fc3965-6bcf-4fd6-85bc-8b0f99f62473
Boniface, M. J.
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Burns, D. K.
40b9dc88-a54a-4365-b747-4456d9203146
Zlatev, Z. D.
8f2e3635-d76c-46e2-85b9-53cc223fee01
White, N. M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Daniels, T. W.V.
9a8ab6f0-2eb9-4703-b536-f86923888213
Kiuber, M.
8feb60c2-8682-49e2-b8b6-bb1fcac34d1c
Chmiel, F. P.
2de259aa-a5eb-460c-bfbf-8b44ed02e2bd
Azor, M.
f857a719-496d-4b79-b28a-17af196a95d8
Borca, F.
31fc3965-6bcf-4fd6-85bc-8b0f99f62473
Boniface, M. J.
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Burns, D. K.
40b9dc88-a54a-4365-b747-4456d9203146
Zlatev, Z. D.
8f2e3635-d76c-46e2-85b9-53cc223fee01
White, N. M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Daniels, T. W.V.
9a8ab6f0-2eb9-4703-b536-f86923888213
Kiuber, M.
8feb60c2-8682-49e2-b8b6-bb1fcac34d1c

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).

Record type: Article

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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Published date: 4 December 2020

Identifiers

Local EPrints ID: 447510
URI: http://eprints.soton.ac.uk/id/eprint/447510
PURE UUID: 9448eb7f-2ef6-40d7-ad49-5a072407ff7e
ORCID for M. J. Boniface: ORCID iD orcid.org/0000-0002-9281-6095
ORCID for D. K. Burns: ORCID iD orcid.org/0000-0001-6976-1068
ORCID for N. M. White: ORCID iD orcid.org/0000-0003-1532-6452

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Date deposited: 12 Mar 2021 17:36
Last modified: 02 Jul 2021 01:54

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Contributors

Author: F. P. Chmiel
Author: M. Azor
Author: F. Borca
Author: M. J. Boniface ORCID iD
Author: D. K. Burns ORCID iD
Author: Z. D. Zlatev
Author: N. M. White ORCID iD
Author: T. W.V. Daniels
Author: M. Kiuber

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