比较长短期记忆模型和卷积神经网络模型在急诊科病人到达日预测中的应用

Sina Moosavi Kashani, Sanaz Zargar Balaye Jame, Nader Markazi, Ali Omrani Nava
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引用次数: 0

摘要

背景:急诊科(ED)面临的最严峻挑战之一就是过度拥挤,这会给患者和工作人员带来负面影响。因此,预测进入急诊室的患者比率有助于有效管理该部门的资源。目标:根据数据收集的时间,我们打算预测在 COVID-19 等流行病和非流行病情况下急诊室的病人入院量。此外,我们还计划比较 LSTM 和 CNN 模型的性能。研究方法收集的数据包括三大类。第一类与空气污染物有关,由德黑兰空气质量控制组织提供。第二类与伊朗气象组织提供的数据有关,第三类包括德黑兰一家医院急诊室收治的病人数量。我们还在数据集中加入了流行病和非流行病的二进制指标。数据收集期为 2018 年 2 月至 2022 年 3 月。我们采用 Dickey-Fuller 检验来评估数据的静态性。经过预处理后,我们独立开发了长短期记忆(LSTM)神经网络和卷积神经网络(CNN)模型,并考虑了前几天的不同时间窗口。我们利用 Python 中的 Keras 和 Tensorflowr 库以及 Google Colab 环境来执行这些模型。结果在过去 7 天的时间窗口中,LSTM 模型的均方根误差(RMSE)和平均绝对误差(MAE)最小,而在过去 13 天的时间窗口中,CNN 模型的表现优于 LSTM 模型。此外,CNN 模型所需的执行时间也少于 LSTM 模型。结论总之,深度学习算法证明适用于分析多变量时间序列数据。CNN 模型的预测误差最小。
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Comparison of Long Short-Term Memory and Convolutional Neural Network Models for Emergency Department Patients’ Arrival Daily Forecasting
Background: One of the most critical challenges in the emergency department (ED) is overcrowding, which creates negative consequences for patients and staff. Therefore, predicting the rate of patients entering the ED can help manage resources in this department effectively. Objectives: According to the time of data collection, we intended to predict the volume of patient admissions to the ED in epidemic conditions, such as COVID-19 and non-epidemic. In addition, we planned to compare the performance of the LSTM and CNN models. Methods: The collected data consists of three main categories. The first category pertains to air pollutants, provided by the Tehran air quality control organization. The second type relates to data from the Iran Meteorological Organization, and the third category includes the number of patients admitted to the ED of a hospital in Tehran. We also incorporated binary indicators for epidemic and non-epidemic conditions in the dataset. The data collection period spans from February 2018 to March 2022. We employed the Dickey-Fuller test to assess the stationarity of the data. After preprocessing, we independently developed long short-term memory (LSTM) neural network and convolutional neural network (CNN) models, considering various time windows of previous days. Keras and Tensorflow libraries in Python, along with the Google Colab environment, were utilized to execute the models. Results: The LSTM model exhibited the lowest root mean square error (RMSE) and mean absolute error (MAE) with a time window of the last seven days, while the CNN model outperformed the LSTM model with a time window of the previous 13 days. Additionally, the CNN model required less execution time than the LSTM model. Conclusions: In conclusion, deep learning algorithms prove suitable for analyzing multivariate time series data. The CNN model demonstrated the lowest prediction error.
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