PURPOSE The purpose of our study was to predict blood pressure variability from time-series data of blood pressure measured at home and data obtained through medical examination at a hospital. Previous studies have reported the blood pressure variability is a significant independent risk factor for cardiovascular disease. METHODS We adopted standard deviation for a certain period and predicted variabilities and mean values of blood pressure for 4 weeks using multi-input multi-output deep neural networks. In designing the prediction model, we prepared a dataset from a clinical study. The dataset included past time-series data for blood pressure and medical examination data such as gender, age, and others. As evaluation metrics, we used the standard deviation ratio (SR) and the root-mean-square error (RMSE). Moreover, we used cross-validation as the evaluation method. RESULTS The prediction performances of blood pressure variability and mean value after 1-4 weeks showed the SRs were "0.67" to "0.70", the RMSEs were "5.04" to "6.65" mmHg, respectively. Additionally, our models were able to work for a participant with high variability in blood pressure values due to its multi-output nature. CONCLUSION The results of this study show that our models can predict blood pressure over 4 weeks. Our models work for an individual with high variability of blood pressure. Therefore, we consider that our prediction models are valuable for blood pressure management.

中文翻译：

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使用深度神经网络预测血压变异性。

目的我们的研究目的是根据在家中测得的血压的时间序列数据和通过医院体检获得的数据来预测血压的变异性。先前的研究已报道血压变异性是心血管疾病的重要独立危险因素。方法我们采用一定时期内的标准差，并使用多输入多输出深度神经网络预测了4周的血压变异性和平均值。在设计预测模型时，我们从临床研究中准备了一个数据集。数据集包括血压的过去时间序列数据和性别，年龄等医学检查数据。作为评估指标，我们使用了标准偏差比（SR）和均方根误差（RMSE）。此外，我们使用交叉验证作为评估方法。结果1-4周后血压变异性和平均值的预测性能显示，SRs分别为“ 0.67”至“ 0.70”，RMSE分别为“ 5.04”至“ 6.65” mmHg。此外，由于其多输出性质，我们的模型能够为血压值具有高可变性的参与者工作。结论这项研究的结果表明我们的模型可以预测4周内的血压。我们的模型适用于血压波动较大的个人。因此，我们认为我们的预测模型对于血压管理很有价值。我们的模型适用于血压波动较大的个人。因此，我们认为我们的预测模型对于血压管理很有价值。我们的模型适用于血压波动较大的个人。因此，我们认为我们的预测模型对于血压管理很有价值。