Universal precautions

Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations.

Over the last decade, convolutional neural networks (CNNs) have emerged as the leading algorithms in image classification and segmentation. Recent publication of large medical imaging databases have accelerated their use in the biomedical arena. While training data for photograph classification benefits from aggressive geometric augmentation, medical diagnosis - especially in chest radiographs - d...

Assisting the Non-invasive Diagnosis of Liver Fibrosis Stages using Machine Learning Methods.

Fibrosis is a significant indication of chronic liver diseases often due to hepatitis C Virus. It is becoming a global concern as a result of the rapid increase in the number of HCV infected patients, the high cost and flaws associated with the assessment process of liver fibrosis. This study aims to determine the features that significantly contribute to the identification of the stages of liver ...

Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment.

Multi-drug-resistant (MDR) infections and their devastating consequences constitute a global problem and a constant threat to public health with immen...

Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic.

PURPOSE OF REVIEW: We review applications of artificial intelligence (AI), including machine learning (ML), in the field of HIV prevention.

Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy.

OBJECTIVE: To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV).

Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.

INTRODUCTION: Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personaliz...

A Bayesian classification model for discriminating common infectious diseases in Zhejiang province, China.

To develop a classification model for accurately discriminating common infectious diseases in Zhejiang province, China.Symptoms and signs, abnormal la...

DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks.

MOTIVATION: We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencin...

PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology.

The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location...

Systems and Precision Medicine in Necrotizing Soft Tissue Infections.

Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflamm...

Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV.

BACKGROUND: Deep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). F...

Network context matters: graph convolutional network model over social networks improves the detection of unknown HIV infections among young men who have sex with men.

OBJECTIVE: HIV infection risk can be estimated based on not only individual features but also social network information. However, there have been ins...

SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties.

MOTIVATION: Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, inter...

Using Smartphone Survey Data and Machine Learning to Identify Situational and Contextual Risk Factors for HIV Risk Behavior Among Men Who Have Sex with Men Who Are Not on PrEP.

"Just-in-time" interventions (JITs) delivered via smartphones have considerable potential for reducing HIV risk behavior by providing pivotal support ...

Artificial intelligence in drug combination therapy.

Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many c...

Clinical Decision Support Systems: From the Perspective of Small and Imbalanced Data Set.

Clinical decision support systems are data analysis software that supports health professionals' decision - making the process to reach their ultimate...

Automatic PAP Mask Sizing with an Error Correcting Autoencoder.

We present the use of an error correcting autoencoder stage to a convolutional neural network model as a means of improving image based automatic Posi...

Bastion3: a two-layer ensemble predictor of type III secreted effectors.

MOTIVATION: Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactio...

DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence.

MOTIVATION: Various bacterial pathogens can deliver their secreted substrates also called effectors through Type III secretion systems (T3SSs) into ho...

Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing.

Robotic assistance presents an opportunity to benefit the lives of many people with physical disabilities, yet accurately sensing the human body and t...