Changsha Social Work College, Changsha, Hunan, China.
Dongfang Electric Corporation, Chengdu, Sichuan, China.
J Healthc Eng. 2022 Mar 29;2022:9689769. doi: 10.1155/2022/9689769. eCollection 2022.
The objective of this study was to discuss the health management of elderly patients in the community and the management of community rehabilitation under the support of the new Internet of Things (IoT). The IoT technology was adopted to monitor the wearable devices through mobile medical physiological data. The heart rate, blood pressure, respiratory rate, and other physiological indicators of the elderly were collected in real time. The support vector machine (SVM) algorithm was selected as the core algorithm for the elderly physiological index disease risk assessment, the fuzzy comprehensive evaluation method was adopted as the core method of the elderly disease risk quantitative assessment model to process the physiological indicators, and finally, a reasonable physiological index processing model and quantitative indicators of disease risk were obtained. The data on vascular disease were selected from the MIMIC database. In addition, the advantages and disadvantages of the SVM algorithm and the Backpropagation Neural Network (BPNN) algorithm were compared and analysed. The final verification results showed that the fusion accuracy of the SVM processing MIMIC database and the University of California Irvine (UCI) dataset was 0.8327 and 0.8045, respectively, while the fusion accuracy of the BPNN algorithm in processing the same data was 0.7792 and 0.7288, respectively. It was obvious that the fusion accuracy of the SVM algorithm was higher than that of the BPNN algorithm, and the accuracy difference of the SVM algorithm was lower than that of the BPNN algorithm in different groups of data. In the verification of the elderly disease risk quantitative assessment model, the results were consistent with the selected data, which verified the effectiveness of the design model in this study. Therefore, it can be used as a quantitative assessment model of general elderly physiological indicators of disease risk and can be applied to the community medical communication management system. It proved that the model of medical communication and rehabilitation services for elderly patients in the community constructed in this study can definitely help the development of community service for the elderly.
本研究旨在探讨新物联网（IoT）支持下社区老年患者的健康管理和社区康复管理。采用物联网技术通过移动医疗生理数据监测可穿戴设备，实时采集老年人的心率、血压、呼吸率等生理指标。选择支持向量机（SVM）算法作为老年人生理指标疾病风险评估的核心算法，采用模糊综合评价法作为老年人疾病风险定量评估模型的核心方法对生理指标进行处理，最终得到合理的生理指标处理模型和疾病风险定量指标。选取来自 MIMIC 数据库的血管疾病数据。此外，对 SVM 算法和反向传播神经网络（BPNN）算法的优缺点进行了对比分析。最终验证结果表明，SVM 处理 MIMIC 数据库和 UCI 数据集的融合准确率分别为 0.8327 和 0.8045，而 BPNN 算法处理相同数据的融合准确率分别为 0.7792 和 0.7288，SVM 算法的融合准确率明显高于 BPNN 算法，且在不同数据组中，SVM 算法的准确率差异均低于 BPNN 算法。在对老年人疾病风险定量评估模型的验证中，结果与所选数据一致，验证了本研究设计模型的有效性。因此，它可以作为一般老年人疾病风险生理指标的定量评估模型，并可应用于社区医疗通信管理系统。研究结果表明，本研究构建的社区老年患者医疗通信和康复服务模型肯定有助于社区老年人服务的发展。
