Infectious Disease

VIDHOP, viral host prediction with deep learning.

MOTIVATION: Zoonosis, the natural transmission of infections from animals to humans, is a far-reaching global problem. The recent outbreaks of Zikavirus, Ebolavirus and Coronavirus are examples of viral zoonosis, which occur more frequently due to globalization. In case of a virus outbreak, it is helpful to know which host organism was the original carrier of the virus to prevent further spreading...

[A Case of Descending Colon and Rectal Cancer with Acute Myeloid Leukemia Performed Robot‒Assisted Hartmann's Procedure].

The case is a 68‒year‒old male, who had been diagnosed with acute myeloid leukemia(AML)prior to rectal cancer surgery, was referred to our hospital for treatment in July 2019. We planned to treat the AML first, and then the colorectal cancer. After completion of 1 course of CAG therapy(cytarabine, aclarubicin, G‒CSF), his white blood cell count increased sufficiently, so he underwent a robot‒assis...

An Insight of the First Community Infected COVID-19 Patient in Beijing by Imported Case: Role of Deep Learning-Assisted CT Diagnosis.

In the era of coronavirus disease 2019 (COVID-19) pandemic, imported COVID-19 cases pose great challenges to many countries. Chest CT examination is c...

Progress in robotics for combating infectious diseases.

The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, ...

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital compon...

Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.

Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in ...

Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many type...

Robots as intelligent assistants to face COVID-19 pandemic.

MOTIVATION: The epidemic at the beginning of this year, due to a new virus in the coronavirus family, is causing many deaths and is bringing the world...

On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic.

OBJECTIVE: This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipm...

Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection.

OBJECTIVE: Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. I...

The risk of racial bias while tracking influenza-related content on social media using machine learning.

OBJECTIVE: Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they h...

Leveraging conversational technology to answer common COVID-19 questions.

The rapidly evolving science about the Coronavirus Disease 2019 (COVID-19) pandemic created unprecedented health information needs and dramatic change...

A Machine Learning Algorithm to Identify Patients with Tibial Shaft Fractures at Risk for Infection After Operative Treatment.

BACKGROUND: Risk stratification of individual patients who are prone to infection would allow surgeons to monitor high-risk patients more closely and ...

Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests.

OBJECTIVE: The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcript...

The value of federated learning during and post-COVID-19.

Federated learning (FL) as a distributed machine learning (ML) technique has lately attracted increasing attention of healthcare stakeholders as FL is...

A machine-learning approach to map landscape connectivity in with genetic and environmental data.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constra...

FLANNEL (Focal Loss bAsed Neural Network EnsembLe) for COVID-19 detection.

OBJECTIVE: The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumon...

An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis.

OBJECTIVE: Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculo...

COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model.

The primary purpose of this research is to identify the best COVID-19 mortality model for India using regression models and is to estimate the future ...

A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study.

Fine crackles are frequently heard in patients with interstitial lung diseases (ILDs) and are known as the sensitive indicator for ILDs, although the ...