A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

Modelling of pre and post Covid-19’s impact on employee’s mode choice behavior

Public transportation is one of the most affected systems by the pandemic. The utilization of public transit during the pandemic made the people feel unsafe. So, the use of private transportation modes for daily mobility has increased. This study aims to understand the COVID-19 impact on the employee's mode choice. The survey methodology adopted in this study is a web-based survey in which questionnaires are distributed via various social media platforms and collected respondents' opinions. After collecting the responses, statistical analysis of socio-demographic characteristics, mode choice preferences, and factors affecting the mode choice were performed. From the results, it is observed that there is a mode shift from public transportation to private transportation to avoid the spread of COVID-19, and also there is a marginal increase in non-motorized transportation modes post COVID-19. The finding indicates the factors related to the spread of the infection, are the most important factors to consider when choosing a mode of transportation following COVID-19. Multinomial logistic regression and artificial neural network models were developed to analyze the mode choice of travelers pre and post COVID-19.

Erythema multiforme related to AstraZeneca SARS-CoV-2 vaccine

A 44-year-old man of Pakistani origin presented to emergency 6 days following his first dose of the AstraZeneca (AZ) SARSCoV- 2 vaccine. He developed flu-like symptoms followed by erythematous pruritic rash. Physical examination showed a maculopapular rash associated with purpura and targetoid lesions affecting his distal extremities, trunk and mucous membranes. He also had crusting and ulceration of his oral and genital mucosal areas. He had no other significant past medical history. A biopsy was taken from his right arm and sent for urgent histology and direct immunofluorescence. Histology revealed parakeratotic scale with interface dermatitis comprising basal layer vacuolation and lymphocyte exocytosis. The epidermis showed prominent dyskeratotic keratinocytes scattered throughout the epidermis. The papillary dermis showed a mild perivascular lymphocytic infiltrate including eosinophils and melanophages. Other investigations showed leucocytosis (12 × 109 L-1), high eosinophils (0.9 × 109 L-1), raised liver enzymes with alkaline phosphatase 159 U L-1 and alanine aminotransferase 172 U L-1. A full infection screen, including herpes simplex virus, SARS-CoV-2 and atypical viral infection, was negative. Immunology was also reported as negative. Based on the findings, a diagnosis of erythema multiforme (EM) secondary to AZ vaccine was made. He was treated with topical steroids and emollients, leading to resolution of his skin and mucosal areas in 4-6 weeks. Recently, there have been a few reported cases of EM in patients with COVID-19 (Jimenez-Cauhe J, Ortega-Quijano D, Carretero- Barrio I et al. Erythema multiforme-like eruption in patients with COVID-19 infection: clinical and histological findings. Clin Exp Dermatol 2020;45: 892-5) and two patients who have had the Pfizer-BioNTech vaccine [Kim M, Kim J, Kim M et al. Generalized erythema multiforme-like skin rash following the first dose of COVID-19 vaccine (Pfizer-BioNTech). J Eur Acad Dermatol Venereol 2021], but the information is limited. Our case emphasizes the need for further studies into the cutaneous adverse effects related to COVID-19vaccines.

Survey on Diagnosing CORONA VIRUS from Radiography Chest X-ray Images Using Convolutional Neural Networks.

Corona Virus continues to harms its effects on the people lives across the globe. The screening of infected persons has to be identified is a vital step because it is a fast and low-cost way. Certain above mentioned things can be recognized by chest X-ray images that plays a significant role and also used for examining in detection of CORONA VIRUS(COVID-19). Here radiological chest X-rays are easily available with low cost only. In this survey paper, Convolutional Neural Network(CNN) based solution that will benefit in detection of the Covid-19 positive patients using radiography chest X-Ray images. To test the efficiency of the solution, using data sets of publicly available X-Ray images of Corona virus positive cases and negative cases. Images of positive Corona Virus patients and pictures of healthy person images are divided into testing images and trainable images. The solution which are providing the good results with classification accuracy within the test set-up. Then GUI based application supports for medical examination areas. This GUI application can be used on any computer and performed by any medical examiner or technician to determine Corona Virus positive patients using radiography X-ray images. The result will be precisely obtaining the Covid-19 Patient analysis through the chest X-ray images and also results may be retrieve within a few seconds.

Significantly higher odds of mortality in stroke-related admissions during COVID-19 pandemic versus pre-COVID/pandemic: A meta-analysis of 455,073 stroke admissions

Background: COVID-19, being a prothrombotic state, has been linked to ischemic infarcts. Pooled data on impact of COVID-related stroke on mortality are sparse. We conducted a meta-analysis to assess the risk of stroke-related inpatient mortality (SRIM) during the COVID pandemic vs. pre-pandemic. Methods: Pubmed/Medline, SCOPUS & EMBASE were searched for articles till August 2021 reporting stroke and SRIM during COVID-19 pandemic vs. pre-pandemic. Random-effects model for odds ratio (OR), I2 statistics for heterogeneity assessment and leave-one-out method for sensitivity analysis were employed. Results: A total of 31 studies with 455,073 stroke hospitalizations;365253 pre-pandemic and 89820 pandemics (mean age 72 vs 70 yrs) were analyzed. With a comparable distribution of males, AF, and thrombolysis, the meta-analysis showed a nearly 40% higher risk of mortality during pandemic vs. pre-pandemic admissions (OR 1.42, 95%CI:1.06-1.92, p=0.018, I2 =98.59). Further subgroup analysis showed a slightly higher risk of mortality in cohorts with mean age <70 years of age vs. ≥70 yrs [mean <70 years (n=11): OR:1.48, p=0.020 vs. ≥70 years (n=17): OR:1.27, p<0.001]. Cross-continental subgroup analysis revealed significantly higher mortality in Europe (n=14, OR:1.31, p<0.001) during pandemic vs. pre-pandemic, and non-significantly higher association in Asia (OR 1.13, p=0.57), USA (OR 1.59, p=0.23), Africa (OR 1.20, p=0.46) (Fig. 1). Subgroup analysis of 16 studies with n=100-1000 showed significantly higher OR (1.31) for SRIM during the pandemic vs. pre-pandemic, whereas studies with n<100 or >1000 did not show any significant difference. Sensitivity analysis showed overall and subgroup stability in OR. Conclusions: This largest meta-analysis to date on the subject found that hospitalized stroke patients, elderly or non-elderly, had nearly 40% higher risk of mortality during the COVID pandemic vs. pre-COVID era across the globe, more significantly in Europe. (Figure Presented).

A year into the pandemic: A mathematical model and study of covid-19 in india

COVID-19 has been declared as a global pandemic by the World Health Organization (WHO) since its outbreak in December 2019. In India, as of May 12th 2021, the total number of coronavirus cases and associated deaths are 2,35,57,676 and 2,56,617 respectively. To control the spread of the virus effectively, social distancing, self-isolation and quarantine, lockdowns and mass inoculation are vital. In this paper we propose a deterministic epidemic model which is an extension of the SEIR model to understand the disease dynamics.The proposed model has eight compartments, Susceptible1, Susceptible2, Exposed, Infected, Quarantined, Isolated, Recovered and Dead and is termed as the S1S2EIQJRD model. The basic reproduction number Ris derived for the proposed model and it is shown that for the disease dies out and for the disease is endemic. Numerical simulations for the growth of the virus across India through the span of the outbreak are obtained. The simulation is done on real data and the results obtained may be used to make suitable inferences about the dynamics of the disease and appropriate measures can be taken to control its spread.

Survey on Diagnosing CORONA VIRUS from Radiography Chest X-ray Images Using Convolutional Neural Networks

Corona Virus continues to harms its effects on the people lives across the globe. The screening of infected persons has to be identified is a vital step because it is a fast and low-cost way. Certain above mentioned things can be recognized by chest X-ray images that plays a significant role and also used for examining in detection of CORONA VIRUS(COVID-19). Here radiological chest X-rays are easily available with low cost only. In this survey paper, Convolutional Neural Network(CNN) based solution that will benefit in detection of the Covid-19 positive patients using radiography chest X-Ray images. To test the efficiency of the solution, using data sets of publicly available X-Ray images of Corona virus positive cases and negative cases. Images of positive Corona Virus patients and pictures of healthy person images are divided into testing images and trainable images. The solution which are providing the good results with classification accuracy within the test set-up. Then GUI based application supports for medical examination areas. This GUI application can be used on any computer and performed by any medical examiner or technician to determine Corona Virus positive patients using radiography X-ray images. The result will be precisely obtaining the Covid-19 Patient analysis through the chest X-ray images and also results may be retrieve within a few seconds.

Community surveillance of COVID-19 pandemic: Perspectives and experiences of medical trainees using mixed-methods research design

Introduction: COVID-19 is an ongoing pandemic and a global public health crisis. India has been setting up multiple strategies to contain this pandemic. Active community-level surveillance is a vital strategy to prevent, control, and manage the outbreak of COVID-19. This study explores the perspectives and describes budding doctors' field experience who worked in the community surveillance activity during the pandemic. Materials and Methods: We used a mixed-method research design wherein 67 medical interns of a tertiary care teaching institute participated in the COVID-19 pandemic surveillance activity were included in the study. Their field experience, perspectives, and opinions were captured using pretested questionnaires, participants' interviews, and focused group discussions. Results: More than one-third of medical interns (41.8%) felt that the government could better handle the surveillance process, while around two-thirds (65.6%) were satisfied with their work. Notably, 40 (59%) were not happy/clear with the training and orientation on the job before engaging in surveillance activity. A majority of 47 (70.1%) interns reported inadequate personal protective equipment, which raised the fear of transmission. While they felt that surveillance provided health services close to the community and addressed the public's pandemic concerns, they said the lack of basic training, an inadequate workforce, and resources were detrimental to the response. Conclusions: This pandemic has exposed the naive interns to the community health surveillance process's ground realities. This experience has changed their perception of the profession and given them the impetus to become a future workforce. Strength, weaknesses, opportunities, and threats analysis of the surveillance process provided vital inputs to act and prepare for future public health emergencies. The following core competencies are addressed in this article: Practice-based learning and improvement, Systems-based practice, Interpersonal and communication skills, and Professionalism. © 2021 Wolters Kluwer Medknow Publications. All rights reserved.

Survey on Diagnosing CORONA VIRUS from Radiography chest X-ray images using Convolutional Neural Networks (preprint)

Corona Virus continues to possess its effects on the people lives across the world. The screening of infected persons is vital step because it is a fast and low-cost way. Chest X-ray images plays a major crucial role and it is used for examination in detection of CORONA VIRUS(COVID-19). Here radiological chest X-rays are easily available with low cost only. In this survey paper, we are using a Convolutional Neural Network(CNN) based solution that will benefit in detection of the Covid-19 Positive patients using radiography chest X-Ray images. To test the efficiency of the solution, we are using public available X-Ray images of Corona Virus Positive cases and negative cases. Images of Positive Corona Virus patients and pictures of healthy person images are divided into testing images and trainable images. The solution which we are providing will give good results in classification accuracy within the test set-up. Here we are going to develop a GUI application for medical Examination areas. This GUI application can be used on any computer and performed by any medical examiner or technician to determine Corona Virus positive patients using radiography X-ray images. The result will be shown or provided by this application is really fast and done within a few seconds.