Yong-Hong Kuo1, Nicholas B Chan2, Janny M Y Leung3, Helen Meng4, Anthony Man-Cho So5, Kelvin K F Tsoi6, Colin A Graham7. 1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: yhkuo@hku.hk. 2. Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. 3. Choi Kai Yau College, The University of Macau, Macau. 4. Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong; Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. 5. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. 6. Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong; School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. 7. Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
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.
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.
Authors: Brian J Douthit; Rachel L Walden; Kenrick Cato; Cynthia P Coviak; Christopher Cruz; Fabio D'Agostino; Thompson Forbes; Grace Gao; Theresa A Kapetanovic; Mikyoung A Lee; Lisiane Pruinelli; Mary A Schultz; Ann Wieben; Alvin D Jeffery Journal: Appl Clin Inform Date: 2022-02-09 Impact factor: 2.342