scholarly journals An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study (Preprint)

2019 ◽  
Author(s):  
Yi-Lien Lee ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Po-Hsin Chou ◽  
Yu-Tsen Yeh ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Early Stage ◽  
Medical Center ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Maslach Burnout Inventory ◽  
Well Being ◽  
Population Based ◽  
Psychological Status ◽  
Sensitivity Specificity

BACKGROUND Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute’s well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. OBJECTIVE The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. METHODS We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO−, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model’s 38 estimated parameters for a website assessment. RESULTS We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model’s predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. CONCLUSIONS The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.

scholarly journals An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study

10.2196/16528 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e16528 ◽  
Author(s):  
Yi-Lien Lee ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Po-Hsin Chou ◽  
Yu-Tsen Yeh ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Early Stage ◽  
Medical Center ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Maslach Burnout Inventory ◽  
Well Being ◽  
Population Based ◽  
Psychological Status ◽  
Sensitivity Specificity

Background Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute’s well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. Objective The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. Methods We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO−, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model’s 38 estimated parameters for a website assessment. Results We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model’s predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. Conclusions The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.

Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

2020 ◽  
Vol 163 (6) ◽  
pp. 1156-1165
Author(s):  
Juan Xiao ◽  
Qiang Xiao ◽  
Wei Cong ◽  
Ting Li ◽  
Shouluan Ding ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Study ◽  
Diagnostic Efficiency ◽  
Training Set ◽  
Multivariable Logistic Regression Model ◽  
Predictive Values ◽  
Validation Set ◽  
Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

S-CCCapsule: Pneumonia detection in chest X-ray images using skip-connected convolutions and capsule neural network

2021 ◽  
pp. 1-25
Author(s):  
Kwabena Adu ◽  
Yongbin Yu ◽  
Jingye Cai ◽  
Victor Dela Tattrah ◽  
James Adu Ansere ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Error ◽  
Medical Center ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Dynamic Routing ◽  
Sigmoid Function ◽  
Radiological Society ◽  
Chest X Ray ◽  
Sensitivity Specificity

The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time.

Urine β-2-glycoprotein 1 as a biomarker for diagnosis of systemic lupus erythematosus

Lupus ◽  
2021 ◽  
pp. 096120332110142
Author(s):  
Jung Sun Lee ◽  
Eun-Ju Lee ◽  
Jeonghun Yeom ◽  
Ji Seon Oh ◽  
Seokchan Hong ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Medical Center ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Urine Samples ◽  
Enzyme Linked Immunosorbent Assay ◽  
Systemic Lupus ◽  
Asan Medical

Objective The need for a biomarker with robust sensitivity and specificity in diagnosing systemic lupus erythematosus (SLE) remains unmet. Compared with blood samples, urine samples are more easily collected; thus, we aimed to identify such a biomarker based on urinary proteomics which could distinguish patients with SLE from healthy controls (HCs). Methods Urine samples were collected from 76 SLE patients who visited rheumatology clinic in 2019 at Asan medical center and from 25 HCs. Urine proteins were analyzed using sequential windowed acquisition of all theoretical fragment ion spectra-mass spectrometry, and the candidate marker was confirmed by enzyme-linked immunosorbent assay (ELISA). Receiver operating characteristic curve analysis was used to determine the diagnostic value of the candidate biomarker. Results Of 1157 proteins quantified, 153 were differentially expressed in urine samples from HCs. Among them were previously known markers including α-1-acid glycoprotein 1, α-2-HS-glycoprotein, ceruloplasmin, and prostaglandin-H2 D-isomerase. Moreover, the amount of β-2 glycoprotein (APOH) was increased in the urine of patients with SLE. The ELISA results also showed the level of urine APOH was higher in patients with SLE than in HCs and patients with rheumatoid arthritis. Moreover, the level was not different between SLE patients with and without nephritis. The urine APOH had an area under the curve value of 0.946 at a cut-off value of 228.53 ng/mg (sensitivity 91.5%, specificity 92.0%) for the diagnosis of SLE. Conclusion The results indicate that the urine APOH level can be an appropriate screening tool in a clinical setting when SLE is suspected.

scholarly journals UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

2017 ◽  
Vol 10 (4) ◽  
pp. 32
Author(s):  
Srinivasan A ◽  
Sudha S
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Computation Time ◽  
Automated System ◽  
Morphological Operations ◽  
Advanced Stage ◽  
Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic. 

scholarly journals Validation of the efficacy of the NUTRISCORE for the nutritional screening of cancer patients in China

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Junren Kang ◽  
Hailong Li ◽  
Xiaodong Shi ◽  
Enling Ma ◽  
Wei Chen
Keyword(s):  
Cancer Patients ◽  
Screening Tool ◽  
Clinical Teaching ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Teaching Hospitals ◽  
Kappa Index ◽  
Nutritional Screening ◽  
Cross Sectional ◽  
Sensitivity Specificity

Abstract Background Malnutrition is common in cancer patients. The NUTRISCORE is a newly developed cancer-specific nutritional screening tool and was validated by comparison with the Patient-Generated Subjective Global Assessment (PG-SGA) and Malnutrition Screening Tool (MST) in Spain. We aimed to evaluate the performance of the NUTRISCORE, MST, and PG-SGA in estimating the risk of malnutrition in Chinese cancer patients. Methods Data from an open parallel and multicenter cross-sectional study in 29 clinical teaching hospitals in 14 Chinese cities were used. Cancer patients were assessed for malnutrition using the PG-SGA, NUTRISCORE, and MST. The sensitivity, specificity, and areas under the receiver operating characteristic curve were estimated for the NUTRISCORE and MST using the PG-SGA as a reference. Results A total of 1000 cancer patients were included. The mean age was 55.9 (19 to 92 years), and 47.5% were male. Of these patients, 450 (45.0%) had PG-SGA B and C, 29 (2.9%) had a NUTRISCORE ≥5, and 367 (36.7%) had an MST ≥ 2. Using the PG-SGA as a reference, the sensitivity, specificity, and area under the curve values of the NUTRISCORE were found to be 6.2, 99.8%, and 0.53, respectively. The sensitivity, specificity, and area under the curve values of the MST were 50.9, 74.9%, and 0.63, respectively. The kappa index between the NUTRISCORE and PG-SGA was 0.066, and that between the MST and PG-SGA was 0.262 (P < 0.05). Conclusions The NUTRISCORE had an extremely low sensitivity in cancer patients in China compared with the MST when the PG-SGA was used as a reference.

scholarly journals Decreased endothelial glycocalyx thickness is an early predictor of mortality in sepsis

2020 ◽  
Vol 48 (3) ◽  
pp. 221-228
Author(s):  
Daniëlle MH Beurskens ◽  
Martine E Bol ◽  
Tammo Delhaas ◽  
Marcel CG van de Poll ◽  
Chris PM Reutelingsperger ◽  
...  
Keyword(s):  
Organ Failure ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Dark Field ◽  
Endothelial Glycocalyx ◽  
Pathophysiological Mechanism ◽  
Heparin Binding ◽  
Sidestream Dark Field ◽  
Glycocalyx Thickness

Microcirculatory alterations play an important role in the early phase of sepsis. Shedding of the endothelial glycocalyx is regarded as a central pathophysiological mechanism causing microvascular dysfunction, contributing to multiple organ failure and death in sepsis. The objective of this study was to investigate whether endothelial glycocalyx thickness at an early stage in septic patients relates to clinical outcome. We measured the perfused boundary region (PBR), which is inversely proportional to glycocalyx thickness, of sublingual microvessels (5–25 µm) using sidestream dark field imaging. The PBR in 21 patients with sepsis was measured within 24 h of admission to the intensive care unit (ICU). In addition, we determined plasma markers of microcirculatory dysfunction and studied their correlation with PBR and mortality. Endothelial glycocalyx thickness in sepsis was significantly lower for non-survivors as compared with survivors, indicated by a higher PBR of 1.97 [1.85, 2.19]µm compared with 1.76 [1.59, 1.97] µm, P=0.03. Admission PBR was associated with hospital mortality with an area under the curve of 0.778 based on the receiver operating characteristic curve. Furthermore, PBR correlated positively with angiopoietin-2 (rho=0.532, P=0.03), indicative of impaired barrier function. PBR did not correlate with Acute Physiology and Chronic Health Evaluation IV (APACHE IV), Sequential Organ Failure Assessment score (SOFA score), lactate, syndecan-1, angiopoietin-1 or heparin-binding protein. An increased PBR within the first 24 h after ICU admission is associated with mortality in sepsis. Further research should be aimed at the pathophysiological importance of glycocalyx shedding in the development of multi-organ failure and at therapies attempting to preserve glycocalyx integrity.

scholarly journals Diagnostic Value of T-SPOT.TB Assay for Tuberculous Peritonitis: A Meta-Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Ying Luo ◽  
Ying Xue ◽  
Liyan Mao ◽  
Qun Lin ◽  
Guoxing Tang ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Abdominal Tuberculosis ◽  
Tuberculous Peritonitis ◽  
Summary Receiver Operating Characteristic ◽  
Sensitivity Specificity

Background: Tuberculous peritonitis (TP) is a common form of abdominal tuberculosis (TB). Diagnosing TP remains challenging in clinical practice. The aim of the present meta-analysis was to evaluate the diagnostic accuracy of peripheral blood (PB) T-SPOT and peritoneal fluid (PF) T-SPOT for diagnosing TP.Methods: PubMed, EmBase, Cochrane, Scopus, Google scholar, China national knowledge internet, and Wan-Fang databases were searched for relevant articles from August 1, 2005 to July 5, 2020. Statistical analysis was performed using Stata, Revman, and Meta-Disc software. Diagnostic parameters including pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were determined. Summary receiver operating characteristic curve was used to determine the area under the curve (AUC).Results: Twelve studies were eligible and included in the meta-analysis. The analysis showed that the pooled sensitivity and specificity of PB T-SPOT in diagnosing TP were 0.91 (95% CI, 0.88–0.94) and 0.78 (95% CI, 0.73–0.81), respectively, while the pooled PLR, NLR, and DOR were 4.05 (95% CI, 2.73–6.01), 0.13 (95% CI, 0.07–0.23), and 37.8 (95% CI, 15.04–94.98), respectively. On the other hand, the summary estimates of sensitivity, specificity, PLR, NLR, and DOR of PF T-SPOT for TP diagnosis were 0.90 (95% CI, 0.85–0.94), 0.78 (95% CI, 0.72–0.83), 6.35 (95% CI, 2.67–15.07), 0.14 (95% CI, 0.09–0.21), and 58.22 (95% CI, 28.76–117.83), respectively. Furthermore, the AUC of PB T-SPOT and PF T-SPOT for TP diagnosis were 0.91 and 0.94, respectively.Conclusions: Our results indicate that both PB T-SPOT and PF T-SPOT can be served as sensitive approaches for the diagnosis of TP. However, the unsatisfactory specificities of these two methods limit their application as rule-in tests for TP diagnosis. Furthermore, the standardization of the operating procedure of PF T-SPOT is further needed.

scholarly journals Successful Outcomes of Single‐Port Insufflation Endoscopic Breast‐Conserving Surgery for Breast Cancer

2021 ◽  
Author(s):  
Zi-Han Wang ◽  
Shan-Shan Wu ◽  
Tian-Ran Gang ◽  
Guo-Xuan Gao ◽  
Fang Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Local Recurrence ◽  
Single Port ◽  
Early Stage ◽  
Statistical Significance ◽  
Well Being ◽  
Breast Conserving Surgery ◽  
Psychological Status ◽  
Minimally Invasive Surgical ◽  
Surgical Methods

Abstract PurposeIn the surgical treatment of breast cancer, the goal of surgeons is to continuously create and improve minimally invasive surgical methods to increase the quality of life of the patient. Currently, routine breast-conserving surgery is performed using two obvious incisions. Here, we compare the clinical efficacy and aesthetic perspectives between a novel technique using one incision called single-port insufflation endoscopic breast-conserving surgery and conventional breast-conserving surgery in early stage breast cancer.MethodsA total of 180 patients with stage I or stage II breast cancer participated in this study. Single-port insufflation endoscopic breast-conserving surgery was performed on 63 patients, while conventional breast-conserving surgery was performed on 117 patients. The evaluation of the aesthetic outcome was carried out by the BREAST-Q scale. Logistic regression was conducted to assess the risk of local recurrence and metastasis.ResultsThere were significant differences between the two groups for chest well-being, psychological well-being, and adverse effects of radiation. The scores for satisfaction of breasts and sexual well-being showed no statistical differences between the two groups. There was no statistical significance in local recurrence or metastasis between the two groups. Single-port insufflation endoscopic breast-conserving surgery did not increase the risk of local recurrence or metastasis.ConclusionThe novel surgical technique, single-port insufflation endoscopic breast-conserving surgery, is a feasible and safe surgery and has advantages in terms of cosmetic outcome and psychological status.

scholarly journals A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

2021 ◽  
Author(s):  
Xudong Zhang ◽  
Jin-Cheng Wang ◽  
Baoqiang Wu ◽  
Tao Li ◽  
Lei Jin ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Training Group ◽  
Clinical Decision ◽  
Polyp Size ◽  
Training Cohort ◽  
Discrimination Ability ◽  
Gallbladder Polyps

Abstract Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

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