■在海上救助个人是一个紧迫的全球公共卫生问题,引起急诊医学研究人员的广泛关注,重点是改善预防和控制策略。本研究旨在利用海上紧急事件数据开发动态贝叶斯网络(DBN)模型,并将其预测精度与自回归综合移动平均(ARIMA)和季节性自回归综合移动平均(SARIMA)模型进行比较。
■在这项研究中,我们分析了2016年1月至2020年12月海南省5家医院在海上急救背景下管理的病例数.我们采用了不同的方法来构建和校准ARIMA,SARIMA,和DBN模型。这些模型随后被用来预测2021年1月至2021年12月的应急人员数量。研究表明,ARIMA,SARIMA,和DBN模型有效地对海上急救医疗服务(EMS)患者数据进行建模和预测,考虑季节性变化。使用平均绝对误差(MAE)评估预测准确性,均方根误差(RMSE),和确定系数(R2)作为性能指标。
■在这项研究中,ARIMA,SARIMA,和DBN模型报告的RMSE分别为5.75、4.43和5.45;MAE分别为4.13、2.81和3.85;R2值分别为0.21、0.54和0.44。MAE和RMSE评估实际值和预测值之间的差异水平。值越小表示模型预测越准确。R2可以比较不同方面的模型性能,值范围从0到1。值接近1表示更好的模型质量。随着错误的增加,R2从最大值进一步移动。SARIMA模型胜过其他模型,显示最低的RMSE和MAE,除了最高的R2,在建模和预测期间。对预测值和拟合图的分析表明,在大多数情况下,SARIMA的预测与实际救援次数非常吻合。因此,SARIMA在拟合和预测方面都很优越,其次是DBN模型,ARIMA显示出最不准确的预测。
■虽然DBN模型巧妙地捕获了变量相关性,SARIMA模型擅长预测海上紧急情况。通过比较这些模型,我们收集了有关海上应急趋势的宝贵见解,促进制定有效的预防和控制策略。
UNASSIGNED: 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.
UNASSIGNED: In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate
ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the
ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R 2) as performance metrics.
UNASSIGNED: In this study, the
ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R 2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R 2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R 2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R 2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA\'s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with
ARIMA showing the least accurate predictions.
UNASSIGNED: While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.