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深度神经网络因子分析模型在手术室管理护理中的应用——胸腔手术后感染的术后护理分析。

Application of Deep Neural Network Factor Analysis Model in Operating Room Management Nursing Analysis of Postoperative Infection Nursing after Thoracic Surgery.

机构信息

Department of Medical Engineering, Tangshan Workers' Hospital, Hebei Province, China 063000.

Department of Thoracic Surgery, Tangshan Workers' Hospital, Hebei Province, China 063000.

出版信息

Comput Math Methods Med. 2021 Oct 26;2021:4622064. doi: 10.1155/2021/4622064. eCollection 2021.

Abstract

Thoracic surgery is the main surgical method for the treatment of respiratory diseases and lung diseases, but infections caused by improper care are prone to occur during the operation, which can induce pulmonary edema and lung injury and affect the effect of the operation and the subsequent recovery. Therefore, it is necessary to control the disease in time and adopt more scientific and comprehensive nursing measures. Based on the neural network algorithm, this paper constructs a neural network-based factor analysis model and applies the operating room management nursing to postoperative infection nursing after thoracic surgery and verifies the effect through the neural network model. The statistical parameters in this article mainly include the postoperative infection rate of thoracic surgery, patient satisfaction, postoperative rehabilitation effect, and complications. Through statistical analysis, it can be known that operating room management and nursing can play an important role in postoperative infection nursing after thoracic surgery, effectively reducing postoperative infection nursing after thoracic surgery, and improving the recovery effect of patients after infection.

摘要

胸外科是治疗呼吸系统疾病和肺部疾病的主要手术方法,但在手术过程中由于护理不当容易发生感染,会诱发肺水肿和肺损伤,影响手术效果和后续恢复。因此,有必要及时控制病情,采取更加科学全面的护理措施。基于神经网络算法,本文构建了基于神经网络的因素分析模型,并将手术室管理护理应用于胸外科手术后的感染护理中,并通过神经网络模型验证效果。本文的统计参数主要包括胸外科手术后的感染率、患者满意度、术后康复效果和并发症。通过统计分析可知,手术室管理和护理在胸外科手术后的感染护理中起着重要作用,能有效降低胸外科手术后的感染护理,提高患者感染后的恢复效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8449/8563115/ba15219fd7bb/CMMM2021-4622064.001.jpg

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