Rainbowfish Rehabilitation & Nursing School, Hangzhou Vocational & Technical College, Hangzhou, Zhejiang, China.
School of Nursing, Peking Union Medical College, Beijing, China.
Comput Math Methods Med. 2021 Nov 15;2021:8019385. doi: 10.1155/2021/8019385. eCollection 2021.
It was to explore the application of nursing defect management evaluation and deep learning in nursing process reengineering optimization. This study first selects the root cause analysis method to analyse the nursing defect management, then realizes the classification of data features according to the convolution neural network (CNN) in deep learning (DL) and uses the constructed training set and verification set to obtain the required plates and feature extraction. Based on statistical analysis and data mining, this study makes statistical analysis of nursing data from a macroperspective, improves Apriori algorithm through simulation, and analyses nursing data mining from a microperspective. The constructed deep learning model is used, CNN network training is conducted on the selected SVHN dataset, the required data types are classified, the data are analysed by using the improved Apriori algorithm, and nurses' knowledge of nursing process rules is investigated and analysed. The cognition of nursing staff on process optimization and their participation in training were analyzed, the defects in the nursing process were summarized, and the nursing process reengineering was analyzed. The results show that compared with Apriori algorithm, the running time difference of the improved Apriori algorithm is relatively small. With the increase of data recording times, the line trend of the improved algorithm gradually eases, the advantages gradually appear, and the efficiency of data processing is more obvious. The results showed that after the optimization of nursing process, the effect of long-term specialized nursing was significantly higher than that of long-term nursing. Health education was improved by 7.57%, clinical nursing was improved by 6.55%, ward management was improved by 9.85%, and service humanization was improved by 8.97%. In summary, the reoptimization of nursing process is conducive to reduce the defects in nursing. In the data analysis and rule generation based on deep learning network, the reoptimization of nursing process can provide reference for decision-making departments to improve long-term nursing, improve the quality and work efficiency of clinical nurses, and is worthy of clinical promotion.
本研究旨在探索护理缺陷管理评估与深度学习在护理流程再造优化中的应用。本研究首先采用根本原因分析法对护理缺陷管理进行分析，然后根据深度学习（DL）中的卷积神经网络（CNN）对数据特征进行分类，并利用构建的训练集和验证集获取所需的板块和特征提取。基于统计分析和数据挖掘，本研究从宏观上对护理数据进行统计分析，通过模拟对 Apriori 算法进行改进，并从微观上对护理数据挖掘进行分析。构建深度学习模型，对选定的 SVHN 数据集进行 CNN 网络训练，对所需的数据类型进行分类，利用改进的 Apriori 算法对数据进行分析，调查和分析护士对护理流程规则的认知。分析护理人员对流程优化的认知及参与培训情况，总结护理流程缺陷，并对护理流程再造进行分析。结果表明，与 Apriori 算法相比，改进后的 Apriori 算法的运行时间差异相对较小。随着数据记录次数的增加，改进算法的线性趋势逐渐缓解，优势逐渐显现，数据处理效率更加明显。结果表明，护理流程优化后，长期专科护理的效果明显高于长期护理。健康教育提高了 7.57%，临床护理提高了 6.55%，病房管理提高了 9.85%，服务人性化提高了 8.97%。综上所述，护理流程的再优化有利于减少护理缺陷。在基于深度学习网络的数据分析和规则生成中，护理流程的再优化可为决策部门提供改进长期护理的参考，提高临床护士的素质和工作效率，值得临床推广。
