Infectious Disease

Association of AI quantified COVID-19 chest CT and patient outcome.

PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome.

Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods.

BACKGROUND AND OBJECTIVE: The new type of Coronavirus (2019-nCov) epidemic spread rapidly, causing more than 250 thousand deaths worldwide. The virus, which first appeared as a sign of pneumonia, was later called the SARS-COV-2 with Severe Acute Respiratory Syndrome by the World Health Organization. The SARS-COV-2 virus is triggered by binding to the Angiotensin-Converting Enzyme 2 (ACE 2) inhibit...

Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.

BACKGROUND: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present cha...

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm.

Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implemen...

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning.

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard....

Comparative Evaluation of the Treatment of COVID-19 with Multicriteria Decision-Making Techniques.

OBJECTIVES: The outbreak of coronavirus disease 2019 (COVID-19) was first reported in December 2019. Until now, many drugs and methods have been used ...

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images.

In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative d...

Approaches to assessing the impact of robotics in geriatric mental health care: a scoping review.

The goals of this scoping literature review are to (1) aggregate the current research involving socially assistive robots in the setting of geriatric ...

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis.

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of micr...

Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?

We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an establishe...

Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs.

Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune s...

Artificial Intelligence-assisted chest X-ray assessment scheme for COVID-19.

OBJECTIVES: To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease ...

Exploration of text matching methods in Chinese disease Q&A systems: A method using ensemble based on BERT and boosted tree models.

BACKGROUND: Text matching is one of the basic tasks in the field of natural language processing. Owing to the particularity of Chinese language and me...

Frontotemporal EEG to guide sedation in COVID-19 related acute respiratory distress syndrome.

OBJECTIVE: To study if limited frontotemporal electroencephalogram (EEG) can guide sedation changes in highly infectious novel coronavirus disease 201...

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role ...

Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.

Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a predictio...

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorize...

Automated processing of social media content for radiologists: applied deep learning to radiological content on twitter during COVID-19 pandemic.

PURPOSE: The purpose of this study was to develop an automated process to analyze multimedia content on Twitter during the COVID-19 outbreak and class...

Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.

RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of d...

Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors.

Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diag...