Infectious Disease

Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.

PURPOSE: To evaluate whether early chest computed tomography (CT) lesions quantified by an artificial intelligence (AI)-based commercial software and blood test values at the initial presentation can differentiate the severity of COVID-19 pneumonia.

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing.

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language...

Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice.

BACKGROUND: Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few effo...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged publ...

Selecting a stable solid form of remdesivir using microcrystal electron diffraction and crystal structure prediction.

Therapeutic options in response to the coronavirus disease 2019 (COVID-19) outbreak are urgently needed. In this communication, we demonstrate how to ...

Classification Criteria for Tubercular Uveitis.

PURPOSE: To determine classification criteria for tubercular uveitis.

Rice leaf diseases prediction using deep neural networks with transfer learning.

Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy...

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.

BACKGROUND: Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning a...

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual...

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images.

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated l...

Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.

The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a l...

Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid.

PROBLEM: Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activa...

COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.

Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between fea...

Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19.

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, acc...

Predicting Infective Endocarditis After Transcatheter Aortic Valve Implantation Via a Risk Model.

Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data.

This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: b...

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade.

: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral disea...

A predictive framework in healthcare: Case study on cardiac arrest prediction.

Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the ap...

Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning.

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, incl...

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans.

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and th...