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An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department
Kuo, Yong Hong1; Chan, Nicholas B.2; Leung, Janny M.Y.3; Meng, Helen2,4; So, Anthony Man Cho4; Tsoi, Kelvin K.F.2,5; Graham, Colin A.6
2020-07-01
Source PublicationInternational Journal of Medical Informatics
ISSN1386-5056
Volume139
Abstract

Objective: The objective of this study is to apply machine learning algorithms for real-time and personalized waiting time prediction in emergency departments. We also aim to introduce the concept of systems thinking to enhance the performance of the prediction models. Methods: Four popular algorithms were applied: (i) stepwise multiple linear regression; (ii) artificial neural networks; (iii) support vector machines; and (iv) gradient boosting machines. A linear regression model served as a baseline model for comparison. We conducted computational experiments based on a dataset collected from an emergency department in Hong Kong. Model diagnostics were performed, and the results were cross-validated. Results: All the four machine learning algorithms with the use of systems knowledge outperformed the baseline model. The stepwise multiple linear regression reduced the mean-square error by almost 15%. The other three algorithms had similar performances, reducing the mean-square error by approximately 20%. Reductions of 17 – 22% in mean-square error due to the utilization of systems knowledge were observed. Discussion: The multi-dimensional stochasticity arising from the ED environment imposes a great challenge on waiting time prediction. The introduction of the concept of systems thinking led to significant enhancements of the models, suggesting that interdisciplinary efforts could potentially improve prediction performance. Conclusion: Machine learning algorithms with the utilization of the systems knowledge could significantly improve the performance of waiting time prediction. Waiting time prediction for less urgent patients is more challenging.

KeywordArtificial Intelligence Emergency Departments Machine Learning Systems Thinking Waiting Time
DOI10.1016/j.ijmedinf.2020.104143
URLView the original
Indexed BySCIE ; SSCI
Language英語English
WOS Research AreaComputer Science ; Health Care Sciences & Services ; Medical Informatics
WOS SubjectComputer Science, Information Systems ; Health Care Sciences & Services ; Medical Informatics
WOS IDWOS:000569077400004
Scopus ID2-s2.0-85083437884
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Cited Times [WOS]:17   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorKuo, Yong Hong
Affiliation1.Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
2.Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, Hong Kong
3.Choi Kai Yau College, The University of Macau, Macao
4.Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong
5.School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong
6.Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong
Recommended Citation
GB/T 7714
Kuo, Yong Hong,Chan, Nicholas B.,Leung, Janny M.Y.,et al. An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department[J]. International Journal of Medical Informatics,2020,139.
APA Kuo, Yong Hong,Chan, Nicholas B.,Leung, Janny M.Y.,Meng, Helen,So, Anthony Man Cho,Tsoi, Kelvin K.F.,&Graham, Colin A..(2020).An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department.International Journal of Medical Informatics,139.
MLA Kuo, Yong Hong,et al."An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department".International Journal of Medical Informatics 139(2020).
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