Infectious Disease

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in uti...

On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19.

According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American populatio...

Protocol for the automatic extraction of epidemiological information via a pre-trained language model.

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention...

Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography.

OBJECTIVES: This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification...

Supervised learning and model analysis with compositional data.

Supervised learning, such as regression and classification, is an essential tool for analyzing modern high-throughput sequencing data, for example in ...

Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19.

Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence mod...

Acceptance of social assistant robots for the older adults living in the community in China.

BACKGROUND: Social assistant robots (SARs) are an important part of providing high quality health and social care for older people, and are an effecti...

COVID-19 symptom identification using Deep Learning and hardware emulated systems.

The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavir...

COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.

The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially ches...

Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022.

Despite the proven safety and clinical efficacy of the Measles vaccine, many countries are seeing new heights of vaccine hesitancy or refusal, and are...

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even d...

Chapter 7: Technological Aids and Coronal Caries.

In recent decades, dentistry has developed significantly in all areas. While in the past, caries was mainly treated operatively, the today's managemen...

ConvCoroNet: a deep convolutional neural network optimized with iterative thresholding algorithm for Covid-19 detection using chest X-ray images.

Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly th...

Discovery of a Novel DCAF1 Ligand Using a Drug-Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets.

DCAF1 functions as a substrate recruitment subunit for the RING-type CRL4 and the HECT family EDVP E3 ubiquitin ligases. The WDR domain of DCAF1 serve...

Clinical Evaluation of Commercial HARDSON COVID-19 Antigen Rapid Test Kit for Routine COVID-19 Diagnosis.

OBJECTIVE: This study aimed to evaluate the sensitivity, specificity, and accuracy of the commercial HARDSON COVID-19 Antigen Rapid Test Kit for diagn...

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning.

A plaque assay-the gold-standard method for measuring the concentration of replication-competent lytic virions-requires staining and usually more than...

Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins.

The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly re...

Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data.

We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs)-and the closure...

Harnessing artificial intelligence in the post-COVID-19 era: A global health imperative.

Despite the World Health Organization's declaration that the COVID-19 global emergency has ended, the threat of future pandemics remains a significant...

Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations.

The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants....