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Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia

This research paper elucidates the clinical effect of an integrated nursing model of medical care and patient in the diagnosis and treatment of congenital esophageal atresia (CEA). For this purpose, a total of 120 children with CEA were selected as study subjects who were admitted to our hospital (J...

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Autores principales: Zhang, Yu, Sun, Xueqiang, Shi, Jingyun, Xiao, Zhenjuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013569/
https://www.ncbi.nlm.nih.gov/pubmed/35440941
http://dx.doi.org/10.1155/2022/4147217
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author Zhang, Yu
Sun, Xueqiang
Shi, Jingyun
Xiao, Zhenjuan
author_facet Zhang, Yu
Sun, Xueqiang
Shi, Jingyun
Xiao, Zhenjuan
author_sort Zhang, Yu
collection PubMed
description This research paper elucidates the clinical effect of an integrated nursing model of medical care and patient in the diagnosis and treatment of congenital esophageal atresia (CEA). For this purpose, a total of 120 children with CEA were selected as study subjects who were admitted to our hospital (January 2017 to April 2020). They were randomly divided into the control group and observation group. Each group had 60 cases. The control group was given routine nursing, while the observation group adopted the integrated nursing model of medical care. The integrated nursing model had the characteristics of recognizing and managing the CEA quickly and efficiently. Thus, it can help increase the survival rate of infants. This model works along with the parents to provide specialized services to the child. They were tasked to carefully observe the infants as well as calm the parents. They were also given the additional task of keeping track of patients who were currently admitted in the hospital and those who were already discharged. The tracking and communication were done with the help of a communication platform which is WeChat. The rehospitalization rate, 1-hour visit rate, accuracy rate of children with suspected postoperative complications, psychological status of children's parents, medical compliance, and satisfaction were compared between the two groups. The rehospitalization rate in the observation group was lower than that in the control group (P < 0.05). The 1-hour visit rate and accuracy of children with suspected postoperative complications in the observation group were higher than those in the control group (P < 0.05). The anxiety and depression scores of the parents in the observation group were lower than those in the control group (P < 0.05). The compliance and satisfaction of parents in the observation group were higher than those in the control group (P < 0.05). The clinical effect of the integrated nursing model of medical care and patient in CEA was highly satisfactory. It reduces the rehospitalization rate and enables timely diagnosis and treatment of suspicious complications effectively. It also improved parents' negative psychological emotions, compliance, and satisfaction.
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spelling pubmed-90135692022-04-18 Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia Zhang, Yu Sun, Xueqiang Shi, Jingyun Xiao, Zhenjuan Comput Intell Neurosci Research Article This research paper elucidates the clinical effect of an integrated nursing model of medical care and patient in the diagnosis and treatment of congenital esophageal atresia (CEA). For this purpose, a total of 120 children with CEA were selected as study subjects who were admitted to our hospital (January 2017 to April 2020). They were randomly divided into the control group and observation group. Each group had 60 cases. The control group was given routine nursing, while the observation group adopted the integrated nursing model of medical care. The integrated nursing model had the characteristics of recognizing and managing the CEA quickly and efficiently. Thus, it can help increase the survival rate of infants. This model works along with the parents to provide specialized services to the child. They were tasked to carefully observe the infants as well as calm the parents. They were also given the additional task of keeping track of patients who were currently admitted in the hospital and those who were already discharged. The tracking and communication were done with the help of a communication platform which is WeChat. The rehospitalization rate, 1-hour visit rate, accuracy rate of children with suspected postoperative complications, psychological status of children's parents, medical compliance, and satisfaction were compared between the two groups. The rehospitalization rate in the observation group was lower than that in the control group (P < 0.05). The 1-hour visit rate and accuracy of children with suspected postoperative complications in the observation group were higher than those in the control group (P < 0.05). The anxiety and depression scores of the parents in the observation group were lower than those in the control group (P < 0.05). The compliance and satisfaction of parents in the observation group were higher than those in the control group (P < 0.05). The clinical effect of the integrated nursing model of medical care and patient in CEA was highly satisfactory. It reduces the rehospitalization rate and enables timely diagnosis and treatment of suspicious complications effectively. It also improved parents' negative psychological emotions, compliance, and satisfaction. Hindawi 2022-04-10 /pmc/articles/PMC9013569/ /pubmed/35440941 http://dx.doi.org/10.1155/2022/4147217 Text en Copyright © 2022 Yu Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Yu
Sun, Xueqiang
Shi, Jingyun
Xiao, Zhenjuan
Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia
title Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia
title_full Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia
title_fullStr Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia
title_full_unstemmed Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia
title_short Study of Nursing Models by Machine Learning in Children with Congenital Esophageal Atresia
title_sort study of nursing models by machine learning in children with congenital esophageal atresia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013569/
https://www.ncbi.nlm.nih.gov/pubmed/35440941
http://dx.doi.org/10.1155/2022/4147217
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