As we move beyond the COVID-19 pandemic, the risk of future infodemics remains significant, driven by emerging health crises and the increasing influence of artificial intelligence in the information ecosystem. During periods of apparent stability, p...
Zhonghua bing li xue za zhi = Chinese journal of pathology
40302570
Over the past decade, China has made remarkable achievements in the updating of molecular characteristics and diagnostic criteria of lung cancer, pathological characteristics of COVID-19, classification scheme of interstitial lung disease, applicatio...
AIMS: Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs us...
BACKGROUND: COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for both venous and arterial thrombosis. This heightened risk contributes to an increased probability o...
BACKGROUND: The COVID-19 pandemic has been accompanied by an "infodemic," where the rapid spread of misinformation has exacerbated public health challenges. Traditional fact-checking methods, though effective, are time-consuming and resource-intensiv...
BACKGROUND: The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis.
This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) and 2D Multifractal Detrended Fluctuation Analysis (2D MF-DFA). By extracting fractal parameters from ...
The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter es...
In this study, we developed a digital twin for SARS-CoV-2 by integrating diverse data and metadata with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein s...
This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating epidemiological, mobility, vaccination, and environmental data ...