Latest AI and machine learning research in primary care for healthcare professionals.
The impact of physical activity on a person's progression to type 2 diabetes is multifaceted. Systems of ordinary differential equations have been crucial in simulating this progression. However, such models often operate on multiple timescales, making them computationally expensive when simulating long-term effects. To overcome this, we propose a homogenized version of a two-timescale model tha...
In order to take full advantage of traditional Chinese medicine (TCM) and western medicine, combined with machine learning technology, to study the risk factors and better risk prediction model of diabetic retinopathy (DR), and provide basis for the screening and treatment of it. Through a retrospective study of DR cases in the real world, the electronic medical records of patients who met screeni...
In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various do...
Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early scre...
This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and eva...
One of the goals of personalized medicine is to tailor diagnostics to individual patients. Diagnostics are performed in practice by measuring quanti...
Systematic reviews (SRs) are essential for evidence-based guidelines but are often limited by the time-consuming nature of literature screening. We ...
The imperative for early detection of type 2 diabetes mellitus (T2DM) is challenged by its asymptomatic onset and dependence on suboptimal clinical ...
The obesity phenomenon, known as the heavy issue, is a leading cause of preventable chronic diseases worldwide. Traditional calorie estimation tools...
Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before t...
Over 30 million Americans are affected by Type II diabetes (T2D), a treatable condition with significant health risks. This study aims to develop an...
Disparities in access to healthcare have been well-documented in the United States, but their effects on electronic health record (EHR) data reliabi...
In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead ...
This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on the development of cardiovascular diseases (CVD) ...
In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particular...
Sudden Cardiac Arrest (SCA) is the leading cause of death among athletes of all age levels worldwide. Current prescreening methods for cardiac risk ...
Deep learning models are widely used to process Computed Tomography (CT) data in the automated screening of pulmonary diseases, significantly reduci...
Electronic healthcare records (EHR) contain a huge wealth of data that can support the prediction of clinical outcomes. EHR data is often stored and...
IMPORTANCE: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers befor...
The application of artificial intelligence methods to electronic patient records paves the way for large-scale analysis of multimodal data. Such popul...