Journal
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Volume 136, Issue -, Pages -Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2019.104067
Keywords
Blood pressure variability; Blood pressure prediction; Deep neural networks; Time-series analysis; Telemedicine
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Funding
- Omron Healthcare Co., Ltd.
- RIKEN, Medical Sciences Innovation Hub Program
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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.
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