Abstract
This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients' compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group (P < 0.05). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment (P < 0.05), and the scores of the quality of life in the intervention group were higher than those in the control group (P < 0.05). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group (P < 0.05). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group (P < 0.001), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group (P < 0.05).
Highlights
Breast cancer is one of the most common malignant tumors in women, and the incidence of breast cancer worldwide is as high as about 20% among female malignant tumors [1].ere are about 27.89 new cases of breast cancer in China every year, accounting for 7.82% of female deaths from cancer [2]
Breast cancer is mainly treated by surgery, and the 5-year survival rate of patients is about 70% [3], but patients will have postoperative complications such as lymphedema and upper limb dysfunction [4], which seriously affects the postoperative quality of life of patients
An intelligent follow-up medical communication system is established based on medical big data and cloud follow-up platform in the Yihui system of Hangzhou Jianhai Technology Co., Ltd, and it is analyzed from the aspects of medical big data mining method and continuous nursing system design, which expected to provide a reference for the evaluation of postoperative intervention effects under big data
Summary
Breast cancer is one of the most common malignant tumors in women, and the incidence of breast cancer worldwide is as high as about 20% among female malignant tumors [1]. Due to the limitation of time and space, traditional methods are prone to lost follow-up and have poor nursing effect and lack personalized continuous nursing care for patients [9]. Mobile medical equipment can overcome the limitations of continuous nursing intervention in time and space, but it still has some shortcomings, such as poor user viscosity and differences in evaluation results, which need to be further optimized. An intelligent follow-up medical communication system is established based on medical big data and cloud follow-up platform in the Yihui system of Hangzhou Jianhai Technology Co., Ltd, and it is analyzed from the aspects of medical big data mining method and continuous nursing system design, which expected to provide a reference for the evaluation of postoperative intervention effects under big data
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