Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment
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Hassanzadeh, Hamed
Good, Norm
Mann, Kay
Khanna, Sankalp
Abdel-Hafez, Ahmad
Cook, David
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Abstract
The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can help busy clinicians to prioritize their efforts while evaluating the individual patient risk of deterioration. The tool also provides visualized explanation of the main contributing factors to its decisions, which can guide the choice of intervention. When applied to a test cohort of 18,648 patient records, the tool achieved 100% sensitivity for prediction windows 2–8 h in advance for patients that were identified at 95%, 85% and 70% risk of deterioration.
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Scientific Reports
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12
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© Crown 2022, corrected publication 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Artificial intelligence
Health management
Health policy
Data management and data science
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
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Brankovic, A; Hassanzadeh, H; Good, N; Mann, K; Khanna, S; Abdel-Hafez, A; Cook, D, Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment, Scientific Reports, 2022, 12, pp. 11734