Background Machine learning (ML) is a growing field in medicine. This narrative review
describes the current body of literature on ML for clinical decision support in infectious …

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms
that resist conventional antibiotic treatment has steadily increased. Thus, it is now …

The lack of interpretability remains a key barrier to the adoption of deep models in many
applications. In this work, we explicitly regularize deep models so human users might step …

In recent years, the machine learning research community has benefited tremendously from
the availability of openly accessible benchmark datasets. Clinical data are usually not …

We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in
patient data makes it difficult for one particular model to succeed at providing suitable …

Viruses are abundant and diverse entities that have important roles in public health,
ecology, and agriculture. The identification and surveillance of viruses rely on an …

Objective: Clinical data's confidential nature often limits the development of machine
learning models in healthcare. Generative adversarial networks (GANs) can synthesise …

To control the Human Immunodeficiency Virus (HIV) pandemic, the World Health
Organization (WHO) set the 90-90-90 target to be reached by 2020. One major threat to …

A wealth of genetic, demographic, clinical and biomarker data is collected from routine
clinical care of HIV patients and exists in the form of medical records available among the …

The beginning of the second century of research in the field of virology (the first virus was
discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the …