Abstract
To improve the nursing effect in patients after thoracic surgery, this paper proposes a refined intervention method in the operating room based on traditional operating room nursing and applies this method to the nursing of patients after thoracic surgery. Moreover, this paper improves the traditional neural network algorithm and uses the deep neural network algorithm to process test data. In addition, it includes patients accepted by the hospital as samples for test analysis and formulates detailed intervention methods for the operating room. Finally, this paper collects the corresponding test data by setting up test and control groups and visually displays the data using mathematical statistics. The statistical parameters of the experiment in this paper include the quality of recovery, complications, satisfaction score, and recovery effect. The comparative test shows that the refined intervention in the operating room based on the neural network proposed in this paper can achieve a certain effect in the postoperative nursing of thoracic surgery, effectively promote the quality of recovery, and reduce the possibility of complications.
Highlights
Many thoracic surgeons regard chronic postsurgical pain (CPSP) as a “normal” and short-lived phenomenon after the operation, and patients can only passively endure it as an inevitable result of the operation and even suspect that the doctor made an error during the operation [5]
If we can describe in detail the prognosis of pain in thoracic surgery patients within three months after surgery, including the time trend of postoperative pain in patients with different pain outcomes, it will help increase the attention of medical workers to CPSP, and at the same time, it will be beneficial to the early detection and intervention of CPSP patients
If deep Boltzmann machine (DBM) is used for data classification, first, the uniform field method is used to calculate the probability distribution Q(h(2)) of the second hidden layer, and v, Q(h(2)) is the input of the network and the corresponding label is the output, and the gradient descent algorithm is used to adjust the weight of the entire network
Summary
Lung cancer is the most common disease that warrants thoracic surgery. It is reported that lung cancer is the cause for about 40% of thoracic surgeries [6]. In the past 30 years, the incidence of lung cancer has increased rapidly due to smoking, air, and environmental pollution. Both in urban and rural areas, the morbidity and mortality rates have increased significantly compared with the 1970s. E results of the National Death Review in the 1970s showed that esophageal cancer ranked second in tumor deaths. E incidence of esophageal cancer in my country is relatively high, and about 60% of esophageal cancers in the world occur in China [9], which accounts for 23% of all tumor deaths in my country. From a holistic perspective, [16] proposed that comfort is a harmonious and pleasant state of physiology, psychology, society, and environment. Reference [17] showed that comfort is pleasant, positive, holistic, two-dimensional, theoretically definable, and operable
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