Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.

Journal: Frontiers in public health

PMID: 39022407

Abstract

INTRODUCTION: Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.

Authors

Pengyu Yang

School of Information, Liaoning University, Shenyang, 110036, China.
Pengfei Cheng

Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Na Zhang

Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing, China.
Ding Luo

Beijing Traditional Chinese Medicine Office for Cancer Prevention and Control, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
Baichao Xu

Department of Physical Education, Hainan Medical University, Haikou, China.
Hua Zhang

School of Clinical Medicine, Hangzhou Medical College, Hangzhou, China.

Keywords

Bayes Theorem China Emergency Medical Services Forecasting Humans Machine Learning Models, Statistical Ships

External Resources

View on PubMed Access via DOI PubMed (39022407)

Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.

Abstract

Authors

Keywords

External Resources

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