Infectious Disease

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany.

During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. In this context, several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approac...

Advanced medical monitoring for the battlefield: A review on clinical applicability of compensatory reserve measurements for early and accurate hemorrhage detection.

Hemorrhagic shock remains the leading cause of mortality in civilian trauma and battlefield settings. The ability of combat medics and other military medical personnel to obtain early identification and assessment of a bleeding casualty is hampered by the use of standard vital signs that fail to provide early predictive indicators of the onset of shock because of compensatory mechanisms. Over the ...

Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline.

BACKGROUND: In Wisconsin, COVID-19 case interview forms contain free-text fields that need to be mined to identify potential outbreaks for targeted po...

A deep learning-driven low-power, accurate, and portable platform for rapid detection of COVID-19 using reverse-transcription loop-mediated isothermal amplification.

This paper presents a deep learning-driven portable, accurate, low-cost, and easy-to-use device to perform Reverse-Transcription Loop-Mediated Isother...

Testing a global null hypothesis using ensemble machine learning methods.

Testing a global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements...

Deep Supervised Domain Adaptation for Pneumonia Diagnosis From Chest X-Ray Images.

Pneumonia is one of the most common treatable causes of death, and early diagnosis allows for early intervention. Automated diagnosis of pneumonia can...

Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning Applications.

Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The abilit...

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodolo...

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ...

A Broad Learning System to Predict the 28-Day Mortality of Patients Hospitalized with Community-Acquired Pneumonia: A Case-Control Study.

This study was to conduct a model based on the broad learning system (BLS) for predicting the 28-day mortality of patients hospitalized with community...

Designing sensitive viral diagnostics with machine learning.

Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved...

Identification of antimicrobial peptides from the human gut microbiome using deep learning.

The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational pr...

INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network.

Testing is one of the important methodologies used by various countries in order to fight against COVID-19 infection. The infection is considered as o...

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has...

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics.

Anticipating intensive care unit (ICU) occupancy is critical in supporting decision makers to impose (or relax) measures that mitigate COVID-19 transm...

COVID-19 Identification System Using Transfer Learning Technique With Mobile-NetV2 and Chest X-Ray Images.

Diagnosis is a crucial precautionary step in research studies of the coronavirus disease, which shows indications similar to those of various pneumoni...

E-Learning Research Trends in Higher Education in Light of COVID-19: A Bibliometric Analysis.

This paper provides a broad bibliometric overview of the important conceptual advances that have been published during COVID-19 within "e-learning in ...

Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis.

Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine prod...

Switching to a NRTI-free 2 drug regimen (2DR) -a sub-analysis of the 48 weeks DUALIS study on metabolic and renal changes.

Switching from a three-drug regimen (3DR: boosted darunavir [bDRV] and two nucleoside reverse transcriptase inhibitors [NRTIs]) to a two-drug regimen...

Rapid and accurate identification of pathogenic bacteria at the single-cell level using laser tweezers Raman spectroscopy and deep learning.

We report a new method for the rapid identification of pathogenic bacterial species at the single-cell level that combines laser tweezers Raman spectr...