Infectious Disease

Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning.

The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry is extensively used to identify microbial isolates at the species level. Since most of clinical laboratories are equipped w...

Deep learning via LSTM models for COVID-19 infection forecasting in India.

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data so...

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both deve...

A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.

Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues d...

Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence.

In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their in...

Soft Hydrogel Actuator for Fast Machine-Learning-Assisted Bacteria Detection.

We demonstrate that our bio-electrochemical platform facilitates the reduction of detection time from the 3-day period of the existing tests to 15 min...

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition.

The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Unde...

Analysis of Clinical Parameters, Drug Consumption and Use of Health Resources in a Southern European Population with Alcohol Abuse Disorder during COVID-19 Pandemic.

The disruption in healthcare attention to people with alcohol dependence, along with psychological decompensation as a consequence of lockdown derived...

Overcoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning.

Understanding antimicrobial usage patterns and encouraging appropriate antimicrobial usage is a critical component of antimicrobial stewardship. Studi...

AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens.

BACKGROUND: Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicro...

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification.

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist d...

The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection.

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There ...

Assessment of medical waste generation, associated environmental impact, and management issues after the outbreak of COVID-19: A case study of the Hubei Province in China.

COVID-19 greatly challenges the human health sector, and has resulted in a large amount of medical waste that poses various potential threats to the e...

Different features identified by machine learning associated with the HIV compartmentalization in semen.

Genetic compartmentalization in semen has been observed in previous studies. However, genetic signatures associated with compartmentalization in semen...

Performance change with the number of training data: A case study on the binary classification of COVID-19 chest X-ray by using convolutional neural networks.

One of the features of artificial intelligence/machine learning-based medical devices resides in their ability to learn from real-world data. However,...

Clinically applicable deep learning-based decision aids for treatment of neovascular AMD.

PURPOSE: Anti-vascular endothelial growth factor (Anti-VEGF) therapy is currently seen as the standard for treatment of neovascular AMD (nAMD). Howeve...

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complem...

COVID-19 detection in CT and CXR images using deep learning models.

Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an exam...

E-TBNet: Light Deep Neural Network for Automatic Detection of Tuberculosis with X-ray DR Imaging.

Currently, the tuberculosis (TB) detection model based on chest X-ray images has the problem of excessive reliance on hardware computing resources, hi...

Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches.

The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with como...