Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization

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.

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Implementation Of Data Mining Association Methods With Apriori Algorithm For Determining The Key Players Of Football Club

International Journal of Computer Techniques ◽

10.29126/23942231/ijct-v7i2p13 ◽

2020 ◽

Vol 7 (2) ◽

Author(s):

Ari Zakaria ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Apriori Algorithm ◽

Football Club ◽

Key Players

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Domain randomization-enhanced deep learning models for bird detection

Scientific Reports ◽

10.1038/s41598-020-80101-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Mao ◽

Jun Kang Chow ◽

Pin Siang Tan ◽

Kuan-fu Liu ◽

Jimmy Wu ◽

...

Keyword(s):

Deep Learning ◽

Continuous Monitoring ◽

Bird Species ◽

Training Data ◽

Learning Models ◽

Fine Grained ◽

Bird Detection ◽

Relationship Of ◽

The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽

10.3390/electronics10101151 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1151

Author(s):

Carolina Gijón ◽

Matías Toril ◽

Salvador Luna-Ramírez ◽

María Luisa Marí-Altozano ◽

José María Ruiz-Avilés

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Analysis ◽

Data Traffic ◽

Medium Term ◽

Short Term ◽

Traffic Forecasting ◽

Series Analysis ◽

Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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Comparative Analysis of Patients with Robotic Hiatal Hernia Repairs with and without Collis Gastroplasty

The American Surgeon ◽

10.1177/0003134821989051 ◽

2021 ◽

pp. 000313482198905

Author(s):

John A. Perrone ◽

Stephanie Yee ◽

Manrique Guerrero ◽

Antai Wang ◽

Brian Hanley ◽

...

Keyword(s):

Quality Of Life ◽

Statistical Analysis ◽

Hospital Stay ◽

Hiatal Hernia ◽

Collis Gastroplasty ◽

Significant Difference ◽

Related Quality

Introduction After extensive mediastinal dissection fails to achieve adequate intra-abdominal esophageal length, a Collis gastroplasty(CG) is recommended to decrease axial tension and reduce hiatal hernia recurrence. However, concerns exist about staple line leak, and long-term symptoms of heartburn and dysphagia due to the acid-producing neoesophagus which lacks peristaltic activity. This study aimed to assess long-term satisfaction and GERD-related quality of life after robotic fundoplication with CG (wedge fundectomy technique) and to compare outcomes to patients who underwent fundoplication without CG. Outcomes studied included patient satisfaction, resumption of proton pump inhibitors (PPI), length of surgery (LOS), hospital stay, and reintervention. Methods This was a single-center retrospective analysis of patients from January 2017 through December 2018 undergoing elective robotic hiatal hernia repair and fundoplication. 61 patients were contacted for follow-up, of which 20 responded. Of those 20 patients, 7 had a CG performed during surgery while 13 did not. There was no significant difference in size and type of hiatal hernias in the 2 groups. These patients agreed to give their feedback via a GERD health-related quality of life (GERD HRQL) questionnaire. Their medical records were reviewed for LOS, length of hospital stay (LOH), and reintervention needed. Statistical analysis was performed using SPSS v 25. Satisfaction and need for PPIs were compared between the treatment and control groups using the chi-square test of independence. Results Statistical analysis showed that satisfaction with outcome and PPI resumption was not significantly different between both groups ( P > .05). There was a significant difference in the average ranks between the 2 groups for the question on postoperative dysphagia on the follow-up GERD HRQL questionnaire, with the group with CG reporting no dysphagia. There were no significant differences in the average ranks between the 2 groups for the remaining 15 questions ( P > .05). The median LOS was longer in patients who had a CG compared to patients who did not (250 vs. 148 min) ( P = .01). The LOH stay was not significantly different ( P > .05) with a median length of stay of 2 days observed in both groups. There were no leaks in the Collis group and no reoperations, conversions, or blood transfusions needed in either group. Conclusion Collis gastroplasty is a safe option to utilize for short esophagus noted despite extensive mediastinal mobilization and does not adversely affect the LOH stay, need for reoperation, or patient long-term satisfaction.

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Rationale for Timing of Follow-Up Visits to Assess Gluten-Free Diet in Celiac Disease Patients Based on Data Mining

Nutrients ◽

10.3390/nu13020357 ◽

2021 ◽

Vol 13 (2) ◽

pp. 357

Author(s):

Alfonso Rodríguez-Herrera ◽

Joaquín Reyes-Andrade ◽

Cristina Rubio-Escudero

Keyword(s):

Data Mining ◽

Celiac Disease ◽

Cultural Context ◽

Gluten Free Diet ◽

Gluten Free ◽

Adherence To Diet ◽

And Gender ◽

Few Data

The assessment of compliance of gluten-free diet (GFD) is a keystone in the supervision of celiac disease (CD) patients. Few data are available documenting evidence-based follow-up frequency for CD patients. In this work we aim at creating a criterion for timing of clinical follow-up for CD patients using data mining. We have applied data mining to a dataset with 188 CD patients on GFD (75% of them are children below 14 years old), evaluating the presence of gluten immunogenic peptides (GIP) in stools as an adherence to diet marker. The variables considered are gender, age, years following GFD and adherence to the GFD by fecal GIP. The results identify patients on GFD for more than two years (41.5% of the patients) as more prone to poor compliance and so needing more frequent follow-up than patients with less than 2 years on GFD. This is against the usual clinical practice of following less patients on long term GFD, as they are supposed to perform better. Our results support different timing follow-up frequency taking into consideration the number of years on GFD, age and gender. Patients on long term GFD should have a more frequent monitoring as they show a higher level of gluten exposure. A gender perspective should also be considered as non-compliance is partially linked to gender in our results: Males tend to get more gluten exposure, at least in the cultural context where our study was carried out. Children tend to perform better than teenagers or adults.

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The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

International Journal of Electrical Engineering Education ◽

10.1177/0020720920936839 ◽

2020 ◽

pp. 002072092093683

Author(s):

Yu Zhu

Keyword(s):

Data Mining ◽

Deep Learning ◽

Social Network ◽

Random Walk ◽

Prediction Method ◽

Random Walk Model ◽

Personality Characteristics ◽

User Personality ◽

Network Platform ◽

Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

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