Graph Attention-based Curriculum Learning for Mental Healthcare Classification.

Research has examined the use of user-generated data from online media as a means of identifying and diagnosing depression as a serious mental health issue that can have a significant impact on an individual's daily life. To achieve this, researchers have examined words in personal statements to identify depression. Besides aiding in diagnosing and treating depression, this research may also provide insight into its preva- lence within society. This paper introduces a Graph Attention Network (GAT) model for the classification of depression from online media. The model is based on masked self-attention layers, which assign different weights to each node in a neighbourhood without costly matrix operations. In addition, an emotion lexicon is extended by using hypernyms to improve the performance of the model. The results of the experiment demonstrate that the GAT model outperforms other architectures, achieving a ROC of 0.98. Furthermore, the embedding of the model is used to illustrate the contribution of the activated words to each symptom and to obtain qualitative agreement from psychiatrists. This technique is used to detect depressive symptoms in online forums with an improved detection rate. This technique uses previously learned embedding to illustrate the contribution of activated words to depressive symptoms in online forums. An improvement of significant magnitude was observed in the model's performance through the use of the soft lexicon extension method, resulting in a rise of the ROC from 0.88 to 0.98. The performance was also enhanced by an increase in the vocabulary and the adoption of a graph-based curriculum. The lexicon expansion method involved the generation of additional words with similar semantic attributes, utilizing similarity metrics to reinforce lexical features. The graph-based curriculum learning was utilized to handle more challenging training samples, allowing the model to develop increasing expertise in learning complex correlations between input data and output labels.

Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease.

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends.

Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases.

Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient's health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person's life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naïve Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient's data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient's health status based on abnormal vital signs and enables patients to receive prompt medical attention.

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray.

BACKGROUND: The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detection and treatment of COVID-19 infected patients are critical to prevent the further spread of the epidemic. However, existing detection methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore, it is crucial to developing a rapid and practical COVID-19 detection method. In this work, we explored the application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method. METHOD: Existing studies have shown that novel coronavirus pneumonia has significant radiographic performance. In this study, we analyze and select the features of chest radiographs. We propose a chest X-Ray (CXR) classification method based on the selected features and investigate the application of transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR classification method based on selected features with transfer learning and ensemble learning, and propose an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose pneumonia and COVID-19 using chest x-ray images. The model aims to provide accurate diagnosis for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/Pneumonia). RESULT: Our proposed CXR classification method based on selection features can significantly improve the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVID- ensemble achieved 91.5% accuracy in the binary classification task and 91.11% in the multicategory classification task. The experimental results demonstrate that the COVID-ensemble can quickly and accurately detect COVID-19 and pneumonia automatically through X-ray images, and that the performance of this model is superior to that of several existing methods. CONCLUSION: Our proposed COVID-ensemble can not only overcome the limitations of the conventional COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection, but also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as a fast and efficient screening method for COVID-19 and pneumonia.

Robust adversarial uncertainty quantification for deep learning fine-tuning.

This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes entropy values and a non-dominant sorting algorithm to identify the candidate with the highest entropy value from the dataset. This is followed by merging the training set with adversarial samples, where a mini-batch of the merged dataset is used to update the dense network parameters. This method can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses. To evaluate the efficacy of the proposed model, two datasets, MNIST and COVID, were used with pixel values and without transfer learning. The results showed an increase of accuracy from 0.85 to 0.88 for MNIST and from 0.83 to 0.85 for COVID, which suggests that the model successfully classified images from both datasets without using transfer learning techniques.

Strong T-cell activation in response to COVID-19 vaccination in multiple sclerosis patients receiving B-cell depleting therapies.

Immunocompromised individuals, including multiple sclerosis (MS) patients on certain immunotherapy treatments, are considered susceptible to complications from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and specific vaccination regimens have been recommended for suitable protection. MS patients receiving anti-CD20 therapy (aCD20-MS) are considered especially vulnerable due to acquired B-cell depletion and impaired antibody production in response to virus infection and COVID-19 vaccination. Here, the humoral and cellular responses are analyzed in a group of aCD20-MS patients (n=43) compared to a healthy control cohort (n=34) during the first 6 months after a 2-dose cycle mRNA-based COVID-19 vaccination. Both IgG antibodies recognizing receptor binding domain (RBD) from CoV-2 spike protein and their blocking activity against RBD-hACE2 binding were significantly reduced in aCD20-MS patients, with a seroconversion rate of only 23.8%. Interestingly, even under conditions of severe B-cell depletion and failed seroconversion, a significantly higher polyfunctional IFNγ+ and IL-2+ T-cell response and strong T-cell proliferation capacity were detected compared to controls. Moreover, no difference in T-cell response was observed between forms of disease (relapsing remitting- vs progressive-MS), anti-CD20 therapy (Rituximab vs Ocrelizumab) and type of mRNA-based vaccine received (mRNA-1273 vs BNT162b2). These results suggest the generation of a partial adaptive immune response to COVID-19 vaccination in B-cell depleted MS individuals driven by a functionally competent T-cell arm. Investigation into the role of the cellular immune response is important to identifying the level of protection against SARS-CoV-2 in aCD20-MS patients and could have potential implications for future vaccine design and application.

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network.

Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body parts, however sometimes blood vessels become clogged or restrained due to cardiac issues. Past heart diagnosis applications are outdated and suffer from poor performance. Therefore, an intelligent heart disease diagnosis application design is required. In this research work, internet of things (IoT) sensor data with a deep learning-based heart diagnosis application is designed. The heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the deep graph convolutional network (DG_ConvoNet) deep learning network. The testing data has been collected from the Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis. DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application achieves an accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% implementing the proposed model.

Strong T-cell activation in response to COVID-19 vaccination in multiple sclerosis patients receiving B-cell depleting therapies

Immunocompromised individuals, including multiple sclerosis (MS) patients on certain immunotherapy treatments, are considered susceptible to complications from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and specific vaccination regimens have been recommended for suitable protection. MS patients receiving anti-CD20 therapy (aCD20-MS) are considered especially vulnerable due to acquired B-cell depletion and impaired antibody production in response to virus infection and COVID-19 vaccination. Here, the humoral and cellular responses are analyzed in a group of aCD20-MS patients (n=43) compared to a healthy control cohort (n=34) during the first 6 months after a 2-dose cycle mRNA-based COVID-19 vaccination. Both IgG antibodies recognizing receptor binding domain (RBD) from CoV-2 spike protein and their blocking activity against RBD-hACE2 binding were significantly reduced in aCD20-MS patients, with a seroconversion rate of only 23.8%. Interestingly, even under conditions of severe B-cell depletion and failed seroconversion, a significantly higher polyfunctional IFNγ+ and IL-2+ T-cell response and strong T-cell proliferation capacity were detected compared to controls. Moreover, no difference in T-cell response was observed between forms of disease (relapsing remitting- vs progressive-MS), anti-CD20 therapy (Rituximab vs Ocrelizumab) and type of mRNA-based vaccine received (mRNA-1273 vs BNT162b2). These results suggest the generation of a partial adaptive immune response to COVID-19 vaccination in B-cell depleted MS individuals driven by a functionally competent T-cell arm. Investigation into the role of the cellular immune response is important to identifying the level of protection against SARS-CoV-2 in aCD20-MS patients and could have potential implications for future vaccine design and application.

Immune Thrombocytopenia Induced by the Chimpanzee Adenovirus-Vectored Vaccine against SARS-CoV-2 Infection.

We present a case of immune thrombocytopenia (ITP) induced by the chimpanzee adenovirus-vectored vaccine, without evidence of thrombosis, eight days after vaccine administration. The thrombocytopenia condition improved after administering steroid treatment. This adenovirus vaccine had been reported to induce rare side effects, such as immune thrombotic thrombocytopenia. This case report showed that it could also induce immune thrombocytopenia without the presence of thrombosis. Therefore, we should be cautious of this rare side effect as global vaccine administrations against coronavirus disease increase.