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Clinical and epidemiological investigation of a child with asymptomatic COVID-19 infection following reoccurrence
ConclusionFirst, we confirmed the reactivation of COVID-19 in a child. The risk of recurrent infection with SARS-CoV-2 was low, and the policy of strictly isolating patients carrying long-term viral ribonucleic acid should be reconsidered. The interval positivity was most likely due to incorrect sampling and/or testing methods. SGS and aB testing are recommended for children with viral reactivation. Second, SARS-CoV-2 viral reactivation cannot be ruled out. The possible mechanisms, such as prolonged infection and viral latent reactivation, need further investigation. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - August 24, 2022 Category: Information Technology Source Type: research

Color-invariant skin lesion semantic segmentation based on modified U-Net deep convolutional neural network
AbstractMelanoma is a type of skin lesion that is less common than other types of skin lesions, but it is fast growing and spreading. Therefore, it is classified as a serious disease that directly threatens human health and life. Recently, the number of deaths due to this disease has increased significantly. Thus, researchers are interested in creating computer-aided diagnostic systems that aid in the proper diagnosis and detection of these lesions from dermoscopy images. Relying on manual diagnosis is time consuming in addition to requiring enough experience from dermatologists. Current skin lesion segmentation systems us...
Source: Health Information Science and Systems - August 14, 2022 Category: Information Technology Source Type: research

Microstate feature fusion for distinguishing AD from MCI
In this study, we tested whether microstates can measure the severity of Alzheimer ’s disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (TPs), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency ofTPs and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds ofTPs were significantly correlated wi...
Source: Health Information Science and Systems - July 26, 2022 Category: Information Technology Source Type: research

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM
AbstractWith the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the “Tree Hole”. The purpose of this article is to support the “Tree Hole” rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of “Tree Hole ” named “Zou Fan”, this paper used a deep learning model and sentiment scorin...
Source: Health Information Science and Systems - July 13, 2022 Category: Information Technology Source Type: research

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy
ConclusionsThis study ’s results proved that the preprocessing steps are significant and had a great effect on highlighting image features. The novel method of stacking and encoding the LBP values in the feature vector greatly affects results when using SVM or CNN for classification. The proposed system outperforms the state of the artwork. The proposed CNN model performs better than SVM. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - June 29, 2022 Category: Information Technology Source Type: research

Optical coherence tomography image based eye disease detection using deep convolutional neural network
AbstractOver the past few decades, health care industries and medical practitioners faced a lot of obstacles to diagnosing medical-related problems due to inadequate technology and availability of equipment. In the present era, computer science technologies such as IoT, Cloud Computing, Artificial Intelligence and its allied techniques, etc. play a crucial role in the identification of medical diseases, especially in the domain of Ophthalmology. Despite this, ophthalmologists have to perform the various disease diagnosis task manually which is time-consuming and the chances of error are also very high because some of the a...
Source: Health Information Science and Systems - June 21, 2022 Category: Information Technology Source Type: research

An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh
AbstractWe aimed to assess different machine learning techniques for predicting infant mortality (<1 year) in Bangladesh. The decision tree (DT), random forest (RF), support vector machine (SVM) and logistic regression (LR) approaches were evaluated through accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristics curve andk-fold cross-validation via simulations. The Boruta algorithm and chi-square (\(\chi ^2\)) test were used for features selection of infant mortality. Overall, the RF technique (Boruta: accuracy = 0.8890, sensitivity = 0.0480, specificity = 0.9789, precision = 0.1960, ...
Source: Health Information Science and Systems - June 21, 2022 Category: Information Technology Source Type: research

Contributions of individual muscle forces to hip, knee, and ankle contact forces during the stance phase of running: a model-based study
This study’s objective was to evaluate the contributions of human lower limb muscles to the hip, knee, and ankle joint con tact forces during the stance phase of running. A total of 25 muscles (or groups) were investigated based on the OpenSim framework along the anterior–posterior, superoinferior, and mediolateral components of each joint coordinate system. It was revealed that, during the running stance phase, the g luteus medius, gluteus maximus, and iliopsoas mainly contributed to the hip contact force. The soleus, vastus group, and rectus femoris primarily contributed to the knee contact force, while the peroneus,...
Source: Health Information Science and Systems - June 16, 2022 Category: Information Technology Source Type: research

A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia
AbstractThe requirement for anaesthesia during modern surgical procedures is unquestionable to ensure a safe experience for patients with successful recovery. Assessment of the depth of anaesthesia (DoA) is an important and ongoing field of research to ensure patient stability during and post-surgery. This research addresses the limitations of current DoA indexes by developing a new index based on electroencephalography (EEG) signal analysis. Empirical wavelet transformation (EWT) methods are employed to extract wavelet coefficients before statistical analysis. The features Spectral Entropy and Second Order Difference Plot...
Source: Health Information Science and Systems - June 6, 2022 Category: Information Technology Source Type: research

Automatic breast lesion segmentation in phase preserved DCE-MRIs
AbstractWe offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denois...
Source: Health Information Science and Systems - May 20, 2022 Category: Information Technology Source Type: research

Pre-collapse femoral head necrosis treated by hip abduction: a computational biomechanical analysis
In this study, we use computational biomechanical technology to investigate mechanical response in FHN patients with hip abduction and establish guide protocols for FHN rehabilitation.Materials and methodsThirty computational models were constructed for evaluating the safety of hip abduction and comparing the biomechanical performance of hip abduction for the treatment of different necrotic classifications. The distribution of principal compressive stress (PCS) and load share ratio (LSR) were computed and used for biomechanical evaluation.ResultsBefore the start of physical therapy, when the size of necrotic segment is inc...
Source: Health Information Science and Systems - May 14, 2022 Category: Information Technology Source Type: research

Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression
ConclusionThe VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - April 21, 2022 Category: Information Technology Source Type: research

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project
AbstractThe ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data st...
Source: Health Information Science and Systems - April 18, 2022 Category: Information Technology Source Type: research

CD-Surv: a contrastive-based model for dynamic survival analysis
AbstractSurvival analysis, aimed at investigating the relationships between covariates and event time, has exhibited profound effects on health service management. Longitudinal data with sequential patterns, such as electronic health records (EHRs), contain a large volume of patient treatment trajectories, and therefore, provide great potential for survival analysis. However, most existing studies address the survival analysis problem in a static manner, that is, they only utilize a fraction of longitudinal data, ignore the correlations between multiple visits, and usually may not be able to capture the latent representati...
Source: Health Information Science and Systems - April 12, 2022 Category: Information Technology Source Type: research

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images
AbstractThe use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature...
Source: Health Information Science and Systems - April 12, 2022 Category: Information Technology Source Type: research