scholarly journals An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
Shu-Ching Ma ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Julie Chi Chow ◽  
Yu-Tsen Yeh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Workplace Bullying ◽  
Computerized Adaptive Testing ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Adaptive Testing ◽  
Web Based ◽  
Nurse Bullying

BACKGROUND Workplace bullying has been measured in many studies to investigate its effects on mental health issues. However, none have used web-based computerized adaptive testing (CAT) with bully classifications and convolutional neural networks (CNN) for reporting the extent of individual bullying in the workplace. OBJECTIVE This study aims to build a model using CNN to develop an app for automatic detection and classification of nurse bullying-levels, incorporated with online Rasch computerized adaptive testing, to help assess nurse bullying at an earlier stage. METHODS We recruited 960 nurses working in a Taiwan Ch-Mei hospital group to fill out the 22-item Negative Acts Questionnaire-Revised (NAQ-R) in August 2012. The k-mean and the CNN were used as unsupervised and supervised learnings, respectively, for: (1) dividing nurses into three classes (n=918, 29, and 13 with suspicious mild, moderate, and severe extent of being bullied, respectively); and (2) building a bully prediction model to estimate 69 different parameters. Finally, data were separated into training and testing sets in a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve [AUC]), along with the accuracy across studies for comparison. An app predicting the respondent bullying-level was developed, involving the model’s 69 estimated parameters and the online Rasch CAT module as a website assessment. RESULTS We observed that: (1) the 22-item model yields higher accuracy rates for three categories, with an accuracy of 94% for the total 960 cases, and accuracies of 99% (AUC 0.99; 95% CI 0.99-1.00) and 83% (AUC 0.94; 95% CI 0.82-0.99) for the lower and upper groups (cutoff points at 49 and 66 points) based on the 947 cases and 42 cases, respectively; and (2) the 700-case training set, with 95% accuracy, predicts the 260-case testing set reaching an accuracy of 97. Thus, a NAQ-R app for nurses that predicts bullying-level was successfully developed and demonstrated in this study. CONCLUSIONS The 22-item CNN model, combined with the Rasch online CAT, is recommended for improving the accuracy of the nurse NAQ-R assessment. An app developed for helping nurses self-assess workplace bullying at an early stage is required for application in the future.

scholarly journals An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study

10.2196/16747 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e16747
Author(s):  
Shu-Ching Ma ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Julie Chi Chow ◽  
Yu-Tsen Yeh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Workplace Bullying ◽  
Computerized Adaptive Testing ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Adaptive Testing ◽  
Web Based ◽  
Nurse Bullying

Background Workplace bullying has been measured in many studies to investigate its effects on mental health issues. However, none have used web-based computerized adaptive testing (CAT) with bully classifications and convolutional neural networks (CNN) for reporting the extent of individual bullying in the workplace. Objective This study aims to build a model using CNN to develop an app for automatic detection and classification of nurse bullying-levels, incorporated with online Rasch computerized adaptive testing, to help assess nurse bullying at an earlier stage. Methods We recruited 960 nurses working in a Taiwan Ch-Mei hospital group to fill out the 22-item Negative Acts Questionnaire-Revised (NAQ-R) in August 2012. The k-mean and the CNN were used as unsupervised and supervised learnings, respectively, for: (1) dividing nurses into three classes (n=918, 29, and 13 with suspicious mild, moderate, and severe extent of being bullied, respectively); and (2) building a bully prediction model to estimate 69 different parameters. Finally, data were separated into training and testing sets in a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve [AUC]), along with the accuracy across studies for comparison. An app predicting the respondent bullying-level was developed, involving the model’s 69 estimated parameters and the online Rasch CAT module as a website assessment. Results We observed that: (1) the 22-item model yields higher accuracy rates for three categories, with an accuracy of 94% for the total 960 cases, and accuracies of 99% (AUC 0.99; 95% CI 0.99-1.00) and 83% (AUC 0.94; 95% CI 0.82-0.99) for the lower and upper groups (cutoff points at 49 and 66 points) based on the 947 cases and 42 cases, respectively; and (2) the 700-case training set, with 95% accuracy, predicts the 260-case testing set reaching an accuracy of 97. Thus, a NAQ-R app for nurses that predicts bullying-level was successfully developed and demonstrated in this study. Conclusions The 22-item CNN model, combined with the Rasch online CAT, is recommended for improving the accuracy of the nurse NAQ-R assessment. An app developed for helping nurses self-assess workplace bullying at an early stage is required for application in the future.

scholarly journals Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 190 ◽  
Author(s):  
Zhiwei Huang ◽  
Jinzhao Lin ◽  
Liming Xu ◽  
Huiqian Wang ◽  
Tong Bai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Disease Classification ◽  
Deep Convolutional Neural Networks ◽  
Learning Methods ◽  
Global And Local

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.

scholarly journals Classifying inpatient perception of hospitalization experience across the US 50 states using the convolutional neural networks

2020 ◽  
Author(s):  
Tung-Hui Jen ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Po-Hsin Chou
Keyword(s):  
Neural Networks ◽  
Service Quality ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Area Under The Curve ◽  
Hospital Service ◽  
The Us ◽  
Sensitivity Specificity ◽  
Consumer Assessment

Abstract Backgrounds The grade of hospital-service quality is required for the classification which is never applicable in the literature. We aimed to classify the grade of inpatients’ perceptions of patients’ hospitalization experience of states in the US using convolutional neural networks(CNN).Methods We downloaded HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Services) data from the 2007-2014 summaries of survey results. The data collection was carried out to the k-mean and the CNN that were used as unsupervised and supervised learnings for (1) dividing the US sates into two classes (n = 19 and 32 of lower and higher grades) and (2) building a hospital service predictive model to estimate 38 parameters. We calculated the sensitivity, specificity, and receiver operating characteristic curve [area under the curve (AUC)] across studies for comparison. An app predicting the hospital service for a specific US state was developed involving the model’s 38 estimated parameters for a website assessment.Results We observed that (1) the two-year 20-item model yields a higher accuracy rate (0.98) with an AUC (0.99; 95% CI 0.95–1.00) based on the 51 states; and (2) an available app for predicting hospital-service quality was successfully developed and demonstrated in this study. A smartphone app was designed to classify the grade of hospital service for each US state.Conclusions The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of the grade about the inpatients’ perceptions of patients’ hospitalization experience of states in the US for hospital service. An app developed for helping patients’ self-assess hospital service quality in each US state is required for application in the future.

scholarly journals Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Symptoms ◽  
Early Stage ◽  
World Health ◽  
Complete Analysis ◽  
Screening Process ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

scholarly journals Finger Vein Verification Techniques using Convolutional Neural Networks

2020 ◽  
Vol 9 (1) ◽  
pp. 1514-1519
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Early Stage ◽  
User Authentication ◽  
Identity Verification ◽  
Finger Vein ◽  
Stage Of Development ◽  
Vein Recognition ◽  
Verification Techniques ◽  
Finger Vein Recognition

Finger vein beneath our skin is one of the unique features for identifying an individual. Because of its uniqueness and security the finger vein recognition is considered as a powerful biometric identifier for user authentication. Several techniques have been evolved for finger vein recognition from its early stage of development, but majority approaches were based on hand crafted features which had limitations on quality of the image, positioning of the finger etc. The emergence of neural networks led to the development of various Convolutional Neural Networks (CNN) based approaches for identity verification. This paper surveys various finger vein verification techniques using CNN and determines the factors that will affect the final result. Publicly available finger vein datasets as well as user designed ones, which are of different qualities, are used for the experimental analysis of these techniques. Though CNN is used in all the cases each one differs in the number of layers used, weight updating methods, results obtained etc. It is found that higher recognition accuracy and lower equal error rate (EER) makes the finger vein verification system an effective one. This field has emerged wide popularity recently and is used in different applications where security is of prime importance

Usage of Machine Learning In Strategy Planning On A Web-Based Business Platforms

2021 ◽  
Vol 12 (1) ◽  
pp. 38-38
Author(s):  
Fedor Zagumennov ◽  
Andrei Bystrov ◽  
Alexey Radaykin ◽  
Paschenko V.V.
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Demand Analysis ◽  
Erp Systems ◽  
Web Based ◽  
Income Data ◽  
Strategy Planning ◽  
Business Functions

This paper describes the practical usage of 1D convolutional neural networks in business platforms for such tasks as income prediction, procurements and order demand analysis. The structure of the CNN model is provided, as well as the results of experiments with real orders, procurements and income data. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Currently web-based platforms featuring advanced business functions are rapidly growing. Their new functions can use classic and modern concepts. The comparison between several approaches, including machine learning and regression are provided. Technologies used in such platforms are provided and analyzed. The structures of a such specific web-platforms frontend and backend systems are observed. Other prospective ideas of usage are formulated. Keywords: Business, Neural, Networks, CNN, Platform

Convolutional Neural Networks for the Classification of the Microstructure of Tight Sandstone

2021 ◽  
Author(s):  
Ana Gabriela Reyna Flores ◽  
Quentin Fisher ◽  
Piroska Lorinczi
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Model Performance ◽  
Area Under The Curve ◽  
Tight Gas ◽  
Classification Problems ◽  
Tight Sandstone ◽  
Permeability Prediction ◽  
Rock Microstructure

Abstract Tight gas sandstone reservoirs vary widely in terms of rock type, depositional environment, mineralogy and petrophysical properties. For this reason, estimating their permeability is a challenge when core is not available because it is a property that cannot be measured directly from wire-line logs. The aim of this work is to create an automatic tool for rock microstructure classification as a first step for future permeability prediction. Permeability can be estimated from porosity measured using wire-line data such as derived from density-neutron tools. However, without additional information this is highly inaccurate because porosity-permeability relationships are controlled by the microstructure of samples and permeability can vary by over five orders of magnitude. Experts can broadly estimate porosity-permeability relationships by analysing the microstructure of rocks using Scanning Electron Microscopy (SEM) or optical microscopy. Such estimates are, however, subjective and require many years of experience. A Machine Learning model for the automation of rock microstructure determination on tight gas sandstones has been built using Convolutional Neural Networks (CNNs) and trained on backscattered images from cuttings. Current results were obtained by training the model on around 24,000 Back Scattering Electron Microscopy (BSEM) images from 25 different rock samples. The obtained model performance for the current dataset are 97% of average of both validation and test categorical accuracy. Also, loss of 0.09 and 0.089 were obtained for validation and test correspondingly. Such high accuracy and low loss indicate an overall great model performance. Other metrics and debugging techniques such Gradient-weighted Class Activation Mapping (Grad-CAM), Receiver Operator Characteristics (ROC) and Area Under the Curve (AUC) were considered for the model performance evaluation obtaining positive results. Nevertheless, this can be improved by obtaining images from new already available samples and make the model generalizes better. Current results indicate that CNNs are a powerful tool and their application over thin section images is an answer for image analysis and classification problems. The use of this classifier removes the subjectivity of estimating porosity-permeability relationships from microstructure and can be used by non-experts. The current results also open the possibility of a data driven permeability prediction based on rock microstructure and porosity from well logs.

Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples

2021 ◽  
pp. CJN.07830621
Author(s):  
Elise Marechal ◽  
Adrien Jaugey ◽  
Georges Tarris ◽  
Michel Paindavoine ◽  
Jean Seibel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Interstitial Fibrosis ◽  
Kidney Tissue ◽  
Area Under The Curve ◽  
Tubular Atrophy ◽  
Prognostic Features ◽  
Glomerular Volume ◽  
Luminal Stenosis ◽  
Histological Data

Background and Objectives: The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histological criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a Deep Learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histological prognostic features. Design, setting, participants, and measurements: Two hundred and forty one samples of healthy kidney tissue were split into 3 independent cohorts. The "Training" cohort (n=65) was used to train two Convolutional Neural Networks: one to detect the cortex and a second one to segment the kidney structures. The "Test" cohort (n=50) assessed their performances by comparing manually outlined regions of interest to predicted ones. The "Application" cohort (n=126) compared prognostic histological data obtained manually or through the algorithm based on the combination of the two Convolutional Neural Networks. Results: In the Test cohort, the networks isolated the cortex and segmented the elements of interest with good performances (more than 90% of the cortex, healthy tubules, glomeruli, and even globally sclerotic glomeruli were detected). In the Application cohort, the expected and predicted prognostic data were significantly correlated. The correlation coefficients r were respectively 0.85 for glomerular volume, 0.51 for glomerular density, 0.75 for interstitial fibrosis, 0.71 for tubular atrophy, and 0.73 for vascular intimal thickness. The algorithm had a good ability to predict significant (> 25%) tubular atrophy and interstitial fibrosis level (ROC curve with an area under the curve 0.92 and 0.91, respectively) or a significant vascular luminal stenosis (> 50%) (area under the curve 0.85). Conclusion: This freely available tool enables the automated segmentation of kidney tissue to obtain prognostic histological data in a fast, objective, reliable and reproducible way.

scholarly journals Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6940 ◽  
Author(s):  
Vincenzo Taormina ◽  
Donato Cascio ◽  
Leonardo Abbene ◽  
Giuseppe Raso
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fluorescence Intensity ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Fine Tuning ◽  
Tuning Method ◽  
Effective Performance ◽  
Training Modalities

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

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