Infectious Disease

From Policy to Prediction: Assessing Forecasting Accuracy in an Integrated Framework with Machine Learning and Disease Models.

To improve the forecasting accuracy of the spread of infectious diseases, a hybrid model was recently introduced where the commonly assumed constant disease transmission rate was actively estimated from enforced mitigating policy data by a machine learning (ML) model and then fed to an extended susceptible-infected-recovered model to forecast the number of infected cases. Testing only one ML model...

Identifying sex from pharyngeal images using deep learning algorithm.

The pharynx is one of the few areas in the body where blood vessels and immune tissues can readily be observed from outside the body non-invasively. Although prior studies have found that sex could be identified from retinal images using artificial intelligence, it remains unknown as to whether individuals' sex could also be identified using pharyngeal images. Demographic information and pharyngea...

Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor. It is estimated that approximately 50-80% of HCC cases worldwide are caused by hepatitis b virus ...

Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.

BACKGROUND: Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intellige...

From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.

The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learn...

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data.

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk asses...

An architecture for COVID-19 analysis and detection using big data, AI, and data architectures.

The COVID-19 epidemic is affecting individuals in many ways and continues to spread all over the world. Vaccines and traditional medical techniques ar...

Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.

BACKGROUND: With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated ac...

Mass Spectrometric and Artificial Intelligence-Based Identification of the Secretome of Plasmodium falciparum Merozoites to Provide Novel Candidates for Vaccine Development Pipeline.

PURPOSE: Merozoites are the only extracellular form of blood stage parasites, making it a worthwhile target. Multiple invasins that are stored in the ...

Spectrochemical and explainable artificial intelligence approaches for molecular level identification of the status of critically ill patients with COVID-19.

This study explores the molecular alterations and disease progression in COVID-19 patients using ATR-FTIR spectroscopy combined with spectrochemical a...

The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.

BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants a...

Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic...

Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis.

BACKGROUND: COVID-19-induced pneumonia has become a persistent health concern, with severe cases posing a significant threat to patient lives. However...

Predictive modeling of mortality in carbapenem-resistant bloodstream infections using machine learning.

, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompting a deeper exploration of treatment alternatives...

Identification of key drivers of antimicrobial resistance in using machine learning.

With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance ...

Modelling the rapid detection of Carbapenemase-resistant Klebsiella pneumoniae based on machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

In this study, 80 carbapenem-resistant Klebsiella pneumoniae (CR-KP) and 160 carbapenem-susceptible Klebsiella pneumoniae (CS-KP) strains detected in ...

Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data.

Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient fo...

A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods.

The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In...

The Relationship Between Metal Exposure and HPV Infection: Evidence from Explainable Machine Learning Methods.

HPV is a ubiquitous pathogen implicated in cervical and other cancers. Although vaccines are available, they do not encompass all subtypes. Meanwhile,...

Shape prior-constrained deep learning network for medical image segmentation.

We propose a shape prior representation-constrained multi-scale features fusion segmentation network for medical image segmentation, including trainin...