Latest AI and machine learning research in surveillance for healthcare professionals.
Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data...
Real-world performance of machine learning (ML) models is crucial for safely and effectively embedding them into clinical decision support (CDS) systems. We examined evidence about the performance of contemporary ML-based CDS in clinical settings. A systematic search of four bibliographic databases identified 32 studies over a 5-year period. The CDS task, ML type, ML method and real-world performa...
Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complica...
Diagnostic codes in the Electronic Health Record (EHR) are known to be limited in reporting patient suicidality, and especially in differentiating the...
Pharmacovigilance (PV) deals with the detection, collection, assessment, understanding, and prevention of adverse effects associated with drugs. The o...
Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accur...
In this article, I present a brief summary of landmark events in the American Journal of Epidemiology, including its founding, the first few decades, ...
: To study the efficacy of pulse methylprednisolone (MPS) therapy in patients with malaria-associated acute respiratory distress syndrome (ARDS). : Th...
Literacy in research studies of artificial intelligence (AI) has become an important skill for radiologists. It is required to make a proper assessmen...
Stochastic modeling predicts various outcomes from stochasticity in the data, parameters and dynamical system. Stochastic models are deemed more appro...
Common tasks encountered in epidemiology, including disease incidence estimation and causal inference, rely on predictive modelling. Constructing a pr...
Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical cost...
The growing accessibility of large health datasets and AI's ability to analyze them offers significant potential to transform public health and epidem...
Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect ...
Machine learning (ML) is an approach to artificial intelligence characterised by the use of algorithms that improve their own performance at a given t...
Technologically enhanced surveillance systems have been proposed for the task of monitoring and responding to antimicrobial resistance (AMR) in both h...
DECIDE-AI is a new, stage-specific reporting guideline for the early and live clinical evaluation of decision-support systems based on artificial inte...
Dental dysplasia are abnormalities in teeth structure, morphology, number and eruption caused by genetic and environmental factors during dental devel...