School of Public Health, Jilin University, Changchun, China.
Front Public Health. 2022 Jul 22;10:946563. doi: 10.3389/fpubh.2022.946563. eCollection 2022.
This cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies.
Seasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Facebook Prophet (Prophet) models were used for time series prediction of the number of RTIs inpatients. The three models were trained using data from 2015 to 2019, and their prediction accuracy was compared using data from 2020 as a test set. The parameters of the SARIMA model were determined using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The LSTM uses linear as the activation function, the mean square error (MSE) as the loss function and the Adam optimizer to construct the model, while the Prophet model is built on the Python platform. The root mean squared error (RMSE), mean absolute error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the predictive performance of the model.
In this research, the LSTM model had the highest prediction accuracy, followed by the Prophet model, and the SARIMA model had the lowest prediction accuracy. The trend in medical expenditure of RTIs inpatients overlapped highly with the number of RTIs inpatients.
By adjusting the activation function and optimizer, the LSTM predicts the number of RTIs inpatients more accurately and robustly than other models. Compared with other models, LSTM models still show excellent prediction performance in the face of data with seasonal and drastic changes. The LSTM can provide a better basis for planning and management in healthcare administration.
The results of this research show that it is feasible to accurately forecast the demand for healthcare resources with seasonal distribution using a suitable forecasting model. The prediction of specific medical service volumes will be an important basis for medical management to allocate medical and health resources.
本横断面研究旨在通过比较研究，开发可靠的预测短期预测模型，以预测中国东北地区的 RTI 数量。
季节性自回归综合移动平均（SARIMA）、长短时记忆（LSTM）和 Facebook Prophet（Prophet）模型用于住院 RTI 人数的时间序列预测。使用 2015 年至 2019 年的数据训练这三个模型，并使用 2020 年的数据作为测试集比较其预测精度。SARIMA 模型的参数使用自相关函数（ACF）和偏自相关函数（PACF）确定。LSTM 使用线性作为激活函数，均方误差（MSE）作为损失函数，Adam 优化器构建模型，而 Prophet 模型在 Python 平台上构建。使用均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）来衡量模型的预测性能。
在这项研究中，LSTM 模型具有最高的预测精度，其次是 Prophet 模型，SARIMA 模型的预测精度最低。住院 RTI 人数的医疗支出趋势与住院 RTI 人数高度重叠。
通过调整激活函数和优化器，LSTM 比其他模型更准确、更稳健地预测住院 RTI 人数。与其他模型相比，LSTM 模型在面对季节性和急剧变化的数据时仍然表现出出色的预测性能。LSTM 可以为医疗管理部门的规划和管理提供更好的基础。
这项研究的结果表明，使用合适的预测模型准确预测具有季节性分布的医疗保健资源需求是可行的。对特定医疗服务量的预测将是医疗管理分配医疗和卫生资源的重要依据。
