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Peng J, Chen C, Zhou M, et al. Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study. JMIR Med Inform. 2020;8(3):e13075doi: 10.2196/13075.
Peng, J., Chen, C., Zhou, M., Xie, X., Zhou, Y., & Luo, C. H. (2020). Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study. JMIR medical informatics, 8(3), e13075. https://doi.org/10.2196/13075
Peng, Junfeng, et al. "Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study." JMIR medical informatics vol. 8,3 (2020): e13075. doi: https://doi.org/10.2196/13075
Peng J, Chen C, Zhou M, Xie X, Zhou Y, Luo CH. Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study. JMIR Med Inform. 2020 Mar 30;8(3):e13075. doi: 10.2196/13075. PMID: 32224488; PMCID: PMC7154928.
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JMIR Med Inform. 2020 Mar 30;8(3):e13075. doi: 10.2196/13075.

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study.

JMIR medical informatics

Junfeng Peng, Chuan Chen, Mi Zhou, Xiaohua Xie, Yuqi Zhou, Ching-Hsing Luo

Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
  2. Surgical Intensive Care Unit, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  3. Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

PMID: 32224488 PMCID: PMC7154928 DOI: 10.2196/13075

Abstract

BACKGROUND: The overcrowding of hospital outpatient and emergency departments (OEDs) due to chronic respiratory diseases in certain weather or under certain environmental pollution conditions results in the degradation in quality of medical care, and even limits its availability.

OBJECTIVE: To help OED managers to schedule medical resource allocation during times of excessive health care demands after short-term fluctuations in air pollution and weather, we employed machine learning (ML) methods to predict the peak OED arrivals of patients with chronic respiratory diseases.

METHODS: In this paper, we first identified 13,218 visits from patients with chronic respiratory diseases to OEDs in hospitals from January 1, 2016, to December 31, 2017. Then, we divided the data into three datasets: weather-based visits, air quality-based visits, and weather air quality-based visits. Finally, we developed ML methods to predict the peak event (peak demand days) of patients with chronic respiratory diseases (eg, asthma, respiratory infection, and chronic obstructive pulmonary disease) visiting OEDs on the three weather data and environmental pollution datasets in Guangzhou, China.

RESULTS: The adaptive boosting-based neural networks, tree bag, and random forest achieved the biggest receiver operating characteristic area under the curve, 0.698, 0.714, and 0.809, on the air quality dataset, the weather dataset, and weather air quality dataset, respectively. Overall, random forests reached the best classification prediction performance.

CONCLUSIONS: The proposed ML methods may act as a useful tool to adapt medical services in advance by predicting the peak of OED arrivals. Further, the developed ML methods are generic enough to cope with similar medical scenarios, provided that the data is available.

©Junfeng Peng, Chuan Chen, Mi Zhou, Xiaohua Xie, Yuqi Zhou, Ching-Hsing Luo. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 30.03.2020.

Keywords: chronic respiratory diseases; ensemble machine learning; health forecasting; outpatient and emergency departments management

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