Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

Construction and evaluation of a cloud follow-up platform for gynecological patients receiving chemotherapy

BACKGROUND

Patient follow-up is an essential component of hospital management. In the current information era, the patient follow-up scheme is expected to be replaced by Internet technology. This study constructed a cloud follow-up platform for gynecological chemotherapy patients and assessed its cost-effectiveness and patients' feedback.

METHODS

A total of 2,538 patients were followed up using a cloud follow-up system between January and October 2021. Prior to this, 690 patients were followed manually via telephone calls. Patients' characteristics, follow-up rate, satisfaction, and session duration were compared between the cloud follow-up and manual follow-up groups. In addition, the read rate of health education materials in the cloud follow-up group was analyzed.

RESULTS

General information, including age, education attainment, cancer stage, and disease category, and follow-up rate (cloud: 6,957/7,614, 91.4%; manual: 1,869/2,070, 90.3%; P = 0.13) did not significantly differ between the two groups. The follow-up satisfaction of the cloud follow-up patients was significantly better than that of the manual follow-up group (cloud: 7,192/7,614, 94.5%; manual: 1,532/2,070, 74.0%; P<0.001). The time spent on the follow-up was approximately 1.2 h for 100 patients in the cloud follow-up group and 10.5 h in the manual follow-up group. Multivariate analysis indicated that the cloud follow-up group had significantly greater follow-up satisfaction (odds ratio: 2.239, 95% CI: 1.237 ~ 5.219). Additionally, the average follow-up duration of the cloud follow-up group decreased by 9.287 h (coefficient: -9.287, 95% CI: -1.439~-0.165). The read rate of health education materials was 72.9% in the cloud follow-up group.

CONCLUSIONS

The follow-up effect of the cloud follow-up group was not inferior to that of the manual follow-up group. The cloud follow-up was more effective for prevention and control requirements in the post-epidemic era. Cloud follow-up can save medical resources, improve cost-effectiveness, provide sufficient health education resources for patients, and improve their satisfaction.

Application of forward selection strategy using C4.5 algorithm to improve the accuracy of classification's data set

The purpose of this study is to improve the classification accuracy of the C4.5 Algorithm utilizing the forward selection technique. Breast Cancer from the UCI Machine Learning Repository is the dataset utilized. There are 286 records in the dataset with nine attributes and one class (label). The suggested model was evaluated with two existing classification models (C4.5 and Naïve Bayes) using the RapidMiner program. The procedure consists of multiple stages, the first of which consists of selecting the dominant trait using the feature selection technique (weight by information gain). The second step is forward selection based on the outcome of feature selection. Before processing, the dataset is separated into training and testing halves, where the ratios of comparison are 70:30, 80:20, and 90:10. The final step is examining the output. The experimental results demonstrate that the forward selection methodology employing the C4.5 (C4.5 + FS) method outperforms the C4.5 and Naïve Bayes classification techniques. C4.5 + FS (Split Data 70:30) has an accuracy value of 76.74%, C4.5 + FS (Split Data 80:20) has an accuracy value of 78.95%, C4.5 + FS (Split Data 90:10) has an accuracy value of 78.57%, C4.5 (Split Data 70:30) has an accuracy value of 65.12%, and Naïve Bayes (Split Data is 70:30) has an accuracy value 85.55%. In comparison to typical classification algorithms (C4.5 and Naïve Bayes), the average accuracy values increased by 12.97% and 8.32%, respectively. In terms of precision, recall, and F-measure, the forward selection strategy utilizing the C4.5 method beat all other classification techniques, achieving 79.84%, 92.50%, and 85.55%, respectively. In addition, the results demonstrated an increase in the average Area Under Curve (AUC) from 0.628 to 0.732%. Therefore, it can be inferred that the forward selection strategy can be applied to the Breast Cancer Data Set in order to increase the accuracy value of classification method C4.5.

A Study on the Application of Extended Care Based on the Biopsychosocial Medicine Model in People with Abnormal Tumor Markers on Physical Examination

The purpose of this study was to explore the value of extended care based on a biopsychosocial medicine model in patients with abnormal tumor markers on physical examination. One hundred and fifty-two cases with abnormal primary screening tumor markers who were examined in our medical examination center between January 2020 and January 2022 were selected as the subjects of this study and divided into intervention and control groups according to the random number table method, with 76 cases in each group. The control group was given the usual extended care intervention and the intervention group was given the extended care intervention based on the biopsychosocial medicine model. The compliance rates of regular follow-up, reasonable diet, appropriate exercise, and regular rest were compared between the two groups. After the intervention, the disease-related knowledge score in the intervention group was higher than that in the control group ( $P<0.05$ ). The compliance rates of regular return visits, reasonable diet, appropriate exercise, and regular routines in the intervention group were higher than those in the control group ( $P<0.05$ ). After the intervention, the scores of psychological states such as anxiety, depression, and post-traumatic growth in the intervention group were better than those in the control group ( $P<0.05$ ). After the intervention, the total scores of objective support, subjective support, support utilization, and social support in the intervention group were higher than those in the control group ( $P<0.05$ ). After the intervention, the intervention group had higher positive coping scores and lower negative coping scores than the control group ( $P<0.05$ ). Continuing care based on the biopsychosocial model of medicine is effective in people with abnormal tumor markers on medical screening. It can improve the knowledge about the disease, increase the compliance rate, improve negative emotions, psychological status, and social support, and promote a more positive way of responding.