syedzayyan/paper_mill_benchmark huggingface dataset:
Effect of a Care Bundle Combined with Continuous Positive Airway Pressure in the Postanesthesia Care Unit on Rapid Recovery after Pulmonary Tumor Resection

To study the effect of a care bundle combined with continuous positive airway pressure (cpap) in the postanesthesia care unit (pacu) on rapid recovery after pulmonary tumor resection.Methods.A total of 135 patients requiring anesthesia resuscitation after pulmonary tumor resection in our hospital from june 2020 to february 2021 were selected.They were randomly divided into three groups: the pacu experimental group, pacu control group, and operating room resuscitation (or) group.Subsequently, their intraoperative clinical symptoms, parameters in monitoring postoperative respiratory status, and follow-up results were compared among the three groups.Results.The pacu experimental group had the highest number of right lesions, while the or group had the highest intraoperative blood transfusion volume, urine volume, intraoperative colloid volume, intrapulmonary shunt, and intraoperative physician handover rate ( p &lt; 0.05 ).Before surgery, serum potassium (k) in the pacu experimental group was significantly higher than that in the or group but lower than that in the pacu control group ( p &lt; 0.01 ).During the time in the pacu, blood partial pressure of oxygen (po2) and oxygen index (oi) levels in the pacu experimental group were significantly higher than those in the other groups ( p &lt; 0.01 ).After surgery, total pacu stay time, time from pacu to extubation, and stay after extubation were markedly reduced in the pacu experimental group ( p &lt; 0.05 ).The highest number of patients with drainage was found in the pacu experimental group, while the highest number of patients without drainage was found in the pacu control group.Conclusion.A care bundle combined with cpap in the pacu can improve the monitoring time of respiratory status and improve blood gas parameters, thus accelerating the postoperative rehabilitation process of patients undergoing pulmonary tumor resection.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Clinical Study;
Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury

The study focused on the therapeutic effects of high-flow oxygen therapy on patients with critical lung injury using edge detection-based ultrasound images.Firstly, the traditional canny edge detection algorithm was improved, and the optimal threshold was obtained by optimizing the median filter and combining otsu algorithm and threshold iteration method.Then, the optimized algorithm was compared with the traditional canny edge detection algorithm and applied to process the lung ultrasound images of 120 cases of critical lung injury, to compare the efficacy of high-flow oxygen therapy and the traditional oxygen therapy.It was found that the peak signal-to-noise ratio (psnr) (20.34~31.3), edge intensity value (17.89~27.34), and edge detection effect of the improved canny algorithm were better than the traditional canny algorithm (15.2~28.61, 9.44~18.56).The failure rate of extubation (4.1%), reintubation rate (0.8%), comfort ( 2.38 ± 0.15 points), dry humidity score ( 1.07 ± 0.21 points), antibiotic use ( 7.41 ± 0.74 days), and hospital stay ( 8.66 ± 1.02 days) in the experimental group were significantly lower than the corresponding indexes in the control group (11.7%, 5%, 4.25 ± 0.26 minutes, 4.94 ± 0.78 minutes, 19.29 ± 1.7 days, and 27.49 ± 2.22 days), and the difference was statistically significant ( p &lt; 0.05 ).In the experimental group, within 48 hours after extubation, the respiratory rate (rr), heart rate (hr), arterial partial pressure of carbon dioxide (pa co2), and hco3- were significantly lower than those of the control group; and the values of transcutaneous oxygen saturation (sp o2), mean arterial pressure (map), arterial partial pressure of oxygen (pa o2), and p h were significantly higher than the control group, and the difference was statistically significant ( p &lt; 0.05 ).In conclusion, the algorithm in this study is superior to the traditional canny algorithm, and the high-flow oxygen therapy can reduce the failure rate of extubation, strengthen patient comfort, improve the degree of gas humidification, stabilize the respiratory function and circulatory system, and shorten the time of antibiotic use and hospital stay.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Early Diagnosis of Breast Cancer Using Image Processing Techniques

Cancer is a noncommunicable chronic disease that indistinctly affects people of any nationality, race, ethnicity, age, or social class.Because of its unpredictability, receiving the diagnosis of this disease is almost always alarming for the patient.Breast cancer, especially among women, occupies a prominent position in this ranking.However, if diagnosed early, there is an excellent chance of a cure.In this sense, digital technologies have been advancing at an increasingly fast pace to support the early diagnosis of the disease.Clinical analysis of breast cancer is commonly performed using diagnostic imaging.One of the most used exams considered the main one for the early detection of this type of cancer is mammography.This exam allows the visualization of breast tissue from image screening using x‐rays.In this sense, the use of computational techniques is essential to assist medical professionals in diagnosing this disease, thus making prevention and early diagnosis even more effective in the current context.The present work is limited to the use of digital technologies (image processing and artificial intelligence) that cooperate with the early diagnosis of breast cancer, which supports the medical professional to analyze images and be able to diagnose, from an early stage, the emergence of breast cancer of the disease, significantly increasing the chances of curing it.It can be gathered from this research how the discovery of x‐rays and the growth in this sector combined with cutting‐edge technology have benefitted in the early detection of the disease and even offered the cure of many cases.

Journal: Journal of Nanomaterials (2022)
Publication types: Research Article;
Diagnosis of Breast Cancer Using Computational Intelligence Models and IoT Applications

The use of computer-aided diagnostic (cad) models has been proposed to aid in the detection and classification of breast cancer.In this work, we evaluated the performance of multilayer perceptron neural network and nonlinear support vector machine models to classify breast cancer nodules.From the contour of 569 samples, ten morphological features were used as input to the classifiers.The average results obtained in the set of 50 simulations performed show that the proposed models showed good performance (all exceeded 90.0%) in terms of accuracy in the test set.The nonlinear support vector machine algorithm stands out when compared to the proposed multilayer perceptron neural network algorithm, with 99% accuracy and a 2% false-negative rate.The neural network model presented lower performance than the nonlinear support vector machine classifier.With the application of the proposed models, the average results obtained are promising in the classification of breast cancer.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Diagnosis and Treatment Rules of Chronic Kidney Disease and Nursing Intervention Models of Related Mental Diseases Using Electronic Medical Records and Data Mining

On the basis of electronic medical records, the data mining technology was adopted to explore the law of chronic kidney disease (ckd) and the intervention mode of mental health of patients.Methods.Based on the electronic medical records, the corresponding data extraction, database establishment, and data cleaning of ckd were performed.After that, the related data analysis, frequency analysis, cluster analysis, and nonparametric analysis were used to explore the laws of ckd diagnosis and treatment and nursing intervention mode of mental illness.The most common causes of ckd were chronic glomerulonephritis (43.76%), aristolochic acid nephritis (16.34%), diabetic nephritis (12.87%), and hypertensive nephritis (11.58%).The major treatment method for end-stage patients was alternative therapies, accounting for 46%.Compared with the depression score before intervention, that of the patients after the mindfulness therapy (50.99 ± 9.77 vs.47.01 ± 9.33, p = 0.024 &lt; 0.5 ) and target behaviour nursing intervention (52.21 ± 8.12 vs.48.01 ± 9.33, p = 0.032 &lt; 0.05 ) was obviously decreased.Conclusion.The data mining technology based on electronic records showed a good application prospect in the analysis of the diagnosis and treatment of ckd; and target behaviour nursing and mindfulness intervention were effective psychological intervention models.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Diagnosing Cancer Using IOT and Machine Learning Methods

Breast cancer affects one in every eight women and is the most common cancer.Aim.To diagnose breast cancer, a potentially fatal condition, using microarray technology, large datasets can now be used.Methods.This study used machine learning algorithms and iot to classify microarray data.They were created from two sets of data: one with 1919 protein types and one with 24481 protein types for 97 people, 46 of whom had a recurring disease and 51 of whom did not.The apps were written in python.Each classification algorithm was applied to the data separately, without any feature elimination or size reduction.Second, two alternative feature reduction approaches were compared to the first case.In this case, machine learning techniques like adaboost and gradient boosting machine are used.Results.Before applying any feature reduction techniques, the logistic regression method produced the best results (90.23%), while the random forest method produced good results (67.22%).In the first data, svm had the highest accuracy rate of 99.23% in both approaches, while in the second data, svm had the highest rate of 87.87% in rlr and 88.82% in lte.Deep learning was also done with mlp.The relationship between depth and classification accuracy was studied using it at various depths.After a while, the accuracy rate declined as the number of layers increased.The maximum accuracy rate in the first data was 97.69%, while it was 68.72% in the second.As a result, adding layers to deep learning does not improve classification accuracy.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Development of Refractive Parameters in 3- to 6-Year-Old Children and Its Application in Myopia Prediction and Intervention Guidance

To investigate refractive development and prevalence of myopia in children aged 3-6 years in hebei province, china, and to explore the developmental law of refraction, so as to clinically guide the prediction and intervention of myopia.Methods.In may 2019, a total of 6120 people were inspected in 68 kindergartens in 11 cities in hebei province.Child refractive refraction was checked under noncycloplegia using a handheld binocular vision screener (sw-800, suoer, tianjin, china).Axial length (al) and corneal radius of curvature (cr) were measured using an ocular biometry (iolmaster 500, carl zeiss, germany).Myopia was defined as spherical equivalent se ≤ − 0.75 d .Results.A total of 5506 children aged 3-6 years met the criteria and were included in the statistical analysis.The prevalence of myopia was 3.49% (1.93% at age 3, 2.90% at age 4, 3.78% at age 5, and 3.88% at age 6).Overall, the mean se was + 0.67 ± 1.05 d ( + 0.81 ± 1.00 d at age 3, + 0.79 ± 1.05 d at age 4, + 0.67 ± 1.08 d at age 5, and + 0.13 ± 1.01 d at age 6); the mean cr was 7.76 ± 0.26 mm ( 7.78 ± 0.26 mm at age3, 7.75 ± 0.25 mm at age 4, 7.77 ± 0.26 mm at age 5, and 7.76 ± 0.25 mm at age 6); the mean al was 22.31 ± 0.73 mm ( 21.98 ± 0.63 mm at age 3, 22.12 ± 0.69 mm at age 4, 22.34 ± 0.73 mm at age 5, and 22.49 ± 0.73 mm at age 6).Conclusions.Prevalence of myopia increases with age in children aged 3-6 years in hebei, china.With the increase of age, cr is basically stable, and al increases gradually.Al/cr, which is closely related to se, can be used as an indicator to predict myopia and guide clinical work.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Development of an IoT-Based Sleep Apnea Monitoring System for Healthcare Applications

Sleep is an essential and vital element of a person’s life and health that helps to refresh and recharge the mind and body of a person.The quality of sleep is very important in every person’s lifestyle, removing various diseases.Bad sleep is a big problem for a lot of people for a very long time.People suffering from various diseases are dealing with various sleeping disorders, commonly known as sleep apnea.A lot of people die during sleep because of uneven body changes in the body during sleep.On that note, a system to monitor sleep is very important.Most of the previous systems to monitor sleeping problems cannot deal with the real time sleeping problem, generating data after a certain period of sleep.Real-time monitoring of sleep is the key to detecting sleep apnea.To solve this problem, an internet of things- (io t-) based real-time sleep apnea monitoring system has been developed.It will allow the user to measure different indexes of sleep and will notify them through a mobile application when anything odd occurs.The system contains various sensors to measure the electrocardiogram (ecg), heart rate, pulse rate, skin response, and sp o2 of any person during the entire sleeping period.This research is very useful as it can measure the indexes of sleep without disturbing the person and can also show it in the mobile application simultaneously with the help of a bluetooth module.The system has been developed in such a way that it can be used by every kind of person.Multiple analog sensors are used with the arduino uno to measure different parameters of the sleep factor.The system was examined and tested on different people’s bodies.To analyze and detect sleep apnea in real-time, the system monitors several people during the sleeping period.The results are displayed on the monitor of the arduino boards and in the mobile application.The analysis of the achieved data can detect sleep apnea in some of the people that the system monitored, and it can also display the reason why sleep apnea happens.This research also analyzes the people who are not in the danger of sleeping problems by the achieved data.This paper will help everyone learn about sleep apnea and will help people detect it and take the necessary steps to prevent it.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Development of a Mobile Application for Interaction between Patients and Doctors in Rural Populations

This study describes the process of construction and evolution of the software development of the mobile application that allows patient-doctor-specialist interaction in rural areas of iraq and helps the patient receive prompt medical guidance without the need for unnecessary transfers because the doctor, general or specialist, can provide it through the application.The construction of the application was carried out using the design thinking process to obtain the mvp3 (minimum viable product) and the running lean process to perform the iterations and reach the application that adds value to the user.For the application software development, the evolutionary development model and some activities of the agile scrum framework were applied.

Journal: Mobile Information Systems (2021)
Publication types: Research Article;
Design and Implementation of Hospital Automatic Nursing Management Information System Based on Computer Information Technology

Clinical nursing work fails to integrate various nursing tasks such as basic care, observation of patients’ conditions, medication, treatment, communication, and health guidance to provide continuous and full nursing care for patients.Based on this, this paper uses the internet of things (io t) technology to optimize the infusion process and achieve closed-loop management of medications and improve the efficiency and safety of infusion and medication administration by using a rational and effective outpatient and emergency infusion and medication management system.The system was built by applying wireless network, barcode technology, rfid, infrared tube sensing, and other technologies and was combined with actual nursing work to summarize application techniques and precautions.The application of this system will become a new highlight of medical informatization, improve patient experience, monitor infusion safety, enhance nursing care, reduce emergency medical disputes, improve patient satisfaction, and will create good social and economic benefits for the hospital.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

The covid-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions.After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex.Our aim is to understand and unravel their sentiments in this research using deep learning techniques.Social media is currently the best way to express feelings and emotions, and with the help of twitter, one can have a better idea of what is trending and going on in people’s minds.Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process.In this research, the timeline of the collected tweets was from december 21 to july21.The tweets contained information about the most common vaccines available recently from across the world.The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (nlp) tool, valence aware dictionary for s entiment reasoner (vader).Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses.In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time.A recurrent neural network- (rnn-) oriented architecture, including long short-term memory (lstm) and bidirectional lstm (bi-lstm), was used to assess the performance of the predictive models, with lstm achieving an accuracy of 90.59% and bi-lstm achieving 90.83%.Other performance metrics such as precision,, f1-score, and a confusion matrix were also used to validate our models and findings more effectively.This study improves understanding of the public’s opinion on covid-19 vaccines and supports the aim of eradicating coronavirus from the world.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

This study was to evaluate the diagnostic value of deep learning-optimized chest ct in the patients with lung cancer.90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects.The mask region convolutional neural network (mask-rcnn) model was a typical end-to-end image segmentation model, and dual path network (dpn) was used in nodule detection.The results showed that the accuracy of dpn algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of ct diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%.Deep learning-based ct examination combined with serum tumor detection, factoring into neurospecific enolase (n s e), cytokeratin 19 fragment (cyfra21), carcinoembryonic antigen (cea), and squamous cell carcinoma (scc) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences ( p &lt; 0.05 ).In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of ct imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Deep Learning Technology in Pathological Image Analysis of Breast Tissue

To explore the application value of the multilevel pyramid convolutional neural network (mpcnn) model based on convolutional neural network (cnn) in breast histopathology image analysis, in this study, based on cnn algorithm and softmax classifier (smc), a sparse autoencoder (sae) is introduced to optimize it.The sliding window method is used to identify cells, and the cnn + smc pathological image cell detection method is established.Furthermore, the local region active contour (lrac) is introduced to optimize it and the lrac fine segmentation model driven by local gaussian distribution is established.On this basis, the sparse automatic encoder is further introduced to optimize it and the mpcnn model is established.The proposed algorithm is evaluated on the pathological image data set.The results showed that the acc value, f value, and re value of pathological cell detection of cnn + smc algorithm were significantly higher than those of the other two algorithms ( p &lt; 0.05).The dice, ol, sen, and spe values of pathological image regional segmentation of cnn algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant ( p &lt; 0.05).The accuracy, recall, and f-measure of the optimized cnn algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively.In the two databases with segmentation standards, the segmentation accuracy of mpcnn is 55%, 73.1%, 78.8%, and 82.1%.In the deep convolution network model, the training time of the mpcnn algorithm is about 80 min.It shows that when the feature dimension is low, the feature map extracted by mpcnn is more effective than the traditional feature extraction method.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;