Abstract
Various factors influencing postoperative incisional infection in gynecologic tumors were analyzed, and the value of quality nursing intervention was studied. In this study, 74 surgically treated gynecologic tumor patients were randomly selected from within the hospital as the study population and were divided into study and control groups. For this purpose, the whole-group random sampling method is utilized to compare the postoperative incisional infection rates of the two groups, analyze their influencing factors, and develop quality nursing interventions. In this paper, a breast cancer diagnosis prediction model was developed by combining the self-attentive mechanism. The preprocessing work such as data quantification and normalization was performed first which is followed by adding the preprocessed data to the self-attentive mechanism. This model has solved the problem that recurrent neural networks (RNNs) could not extract and calculate the features at the same time. Likewise, it has solved the drawback that the RNN could not consider global features at the same time when extracting the features, and then, the feature matrix extracted by the self-attentive mechanism was added to the adaptive neural network. The adaptive neural network model for breast cancer diagnosis prediction was constructed and, finally, relevant parameters of the adaptive neural network model were adjusted according to different tasks to make the model performance optimal. Experimental results showed that the postoperative incision infection rate of patients in the study group was 2.70%, which was significantly lower than that of 21.62% in the control group (P < 0.05). Likewise, operation time, operation method, hospitalization time, preoperative fever, diabetes mellitus, and anemia were the main influencing factors of postoperative incision infection in women with gynecologic tumors. The time of surgery, surgical method, long hospital stay, preoperative fever, diabetes, and anemia are the main factors that lead to postoperative incisional infection in female gynecologic tumor patients.
Highlights
Gynecologic tumor is a common gynecologic disease, which poses serious danger to women’s physical and mental health, and one of the most common clinical treatment methods is surgery. erefore, if postoperative incision infection occurs in female gynecologic tumor patients, it will affect the treatment effect and increase the treatment burden of patients and lead to the failure of surgery [1]. erefore, it is important to analyze the factors affecting postoperative incisional infection in female gynecologic tumor patients and develop a targeted quality nursing intervention program to improve the effect of female gynecologic tumor surgical treatment
70% of the breast cancer pathology data were selected as the training set and 30% of the data were selected as the test set for each data set. e experiments in this paper consist of two parts: (1) Adjust the relevant parameters of the adaptive neural network model according to different datasets and select the best adaptive neural network model
Data are quantified and normalized. en, we add the preprocessed data into the self-attentive mechanism and add the feature matrix extracted by the self-attentive mechanism into the adaptive neural network to build the adaptive breast cancer diagnosis prediction neural network model. e experimental results show that the dataset used in this paper has a good experimental effect on the breast cancer diagnosis prediction model based on our method
Summary
Gynecologic tumor is a common gynecologic disease, which poses serious danger to women’s physical and mental health, and one of the most common clinical treatment methods is surgery. erefore, if postoperative incision infection occurs in female gynecologic tumor patients, it will affect the treatment effect and increase the treatment burden of patients and lead to the failure of surgery [1]. erefore, it is important to analyze the factors affecting postoperative incisional infection in female gynecologic tumor patients and develop a targeted quality nursing intervention program to improve the effect of female gynecologic tumor surgical treatment. Erefore, if postoperative incision infection occurs in female gynecologic tumor patients, it will affect the treatment effect and increase the treatment burden of patients and lead to the failure of surgery [1]. Erefore, it is important to analyze the factors affecting postoperative incisional infection in female gynecologic tumor patients and develop a targeted quality nursing intervention program to improve the effect of female gynecologic tumor surgical treatment. This study randomly selected 74 female gynecologic tumor patients treated surgically and analyzed the clinical treatment effect of quality nursing care. Many patients in remote areas cannot receive good treatment due to the lack of medical resources and many people miss the best time to treat their diseases due to untimely access to medical care
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