Latest AI and machine learning research in surveillance for healthcare professionals.
BACKGROUND: Accurate short-term influenza forecasting is important for early warning and public health preparedness, but routine sentinel surveillance may be delayed and may not fully capture behavioural and environmental signals related to influenza activity. This study aimed to develop and evaluate multi-source machine-learning models for short-term influenza forecasting in Hunan Province, China...
Estimating, understanding, and communicating uncertainty is fundamental to statistical epidemiology, where model-based estimates regularly inform real-world decisions. However, sources of uncertainty are rarely formalised, and existing classifications are often inconsistent. This lack of structure hampers interpretation, model comparison, and targeted data collection. Connecting ideas from machine...
OBJECTIVE: To argue that diagnostic and predictive AI should be evaluated by both classification performance and the downstream work their outputs cre...
INTRODUCTION: Trigger tool methodologies have become important approaches for detecting adverse events in hospital care because they identify more har...
Image quantification is central to modern biomedical research. However, the reproducibility of image-based studies remains a persistent challenge due ...
BACKGROUND: Accurate reporting in nuclear medicine is essential for clinical decision-making. Trainees often generate preliminary reports with variabl...
BACKGROUND: Diabetic foot ulcers (DFU) are serious complications of diabetes that contribute substantially to morbidity, mortality, and health care bu...
RATIONALE: Resource-limited or austere environments represent a direct threat to the likelihood of survival of patients in need of emergent care. Medi...
BACKGROUND: Machine learning (ML), deep learning (DL) and other predictive modelling approaches are increasingly applied to predict antiretroviral the...
Classical (crisp) graph models fail to capture uncertainty inherent in real-world networks, whereas fuzzy topological indices provide a richer descrip...
Safety helmet compliance monitoring remains challenging because helmets often occupy only a few pixels in wide-area surveillance images. This study ta...
Accurate forecasting of infectious disease cases and deaths is crucial for public health decision-making. Traditional statistical and machine learning...
Label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) provides a rapid, reagent-light approach to microbial detect...
The recent rapid development of mobile and wearable sensing technologies and computational modeling has allowed for high-density and continuous measur...
Antimicrobial resistance (AMR) is increasingly recognised as a One Health challenge in which environmental reservoirs play an important role in the pe...
Radiomics applied to two-dimensional breast ultrasound has emerged as a potential noninvasive approach for differentiating benign from malignant breas...
Deep learning (DL) is increasingly applied to automate brain tumor classification from magnetic resonance imaging (MRI), yet meaningful clinical deplo...
While data assets are increasingly recognized as strategic resources in the digital economy, their implications for financial reporting quality remain...
Transfusion medicine has practiced a form of precision medicine for decades through compatibility testing, infectious disease screening, component man...
Antimicrobial resistance in aquaculture threatens environmental and public health, but the risk of ARGs cannot be inferred from abundance alone; host ...