Latest AI and machine learning research in health policy for healthcare professionals.
Health care costs now comprise nearly one-fifth of the United States' gross domestic product, with the last 25 years marked by rising administrative costs, a lack of labor productivity growth, and rising patient and physician dissatisfaction. Policy experts have responded with a series of reforms that have - ironically - increased patient and physician administrative burden with little meaningful ...
The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (...
The Healthcare Internet-of-Things (IoT) framework aims to provide personalized medical services with edge devices. Due to the inevitable data sparsity...
Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendousl...
Hospitals use medical cyber-physical systems (MCPS) more often to give patients quality continuous care. MCPS isa life-critical, context-aware, networ...
Medical interpretation is an underutilized resource, despite its legal mandate and proven efficacy in improving health outcomes for populations with l...
Robotics education is important in training children's thinking, practical, and innovation abilities. It is significant to stimulate children's intere...
Inborn errors of immunity represent a rapidly expanding group of genetic disorders of the immune system. Significant advances have been made in recent...
Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. Ho...
Extracting speech information from vibration response signals is a typical system identification problem, and the traditional method is too sensitive ...
Endovascular robots have the potential to revolutionize the field of vascular interventions by enhancing procedural efficiency, accuracy, and standard...
The scarcity of Health Human Resources (HHR), regional disparities, and decentralized healthcare systems have profoundly affected health equity in Can...
Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. How...
Named entity recognition (NER) is a widely used text-mining and natural language processing (NLP) subtask. In recent years, deep learning methods have...
Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicabl...
Cooperation among teams or individuals of healthcare professionals (HCPs) is one of the crucial factors towards patients' survival outcome. However, i...
Directly determining trace compounds in actual sample by ambient ionization mass spectrometry (AIMS) has always been a challenge. Due to the excellent...
The quality of traditional Chinese medicine is very important for human health, but the traditional quality control method is very tedious, which lead...
Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learn...
This paper studies the scheduling of autonomous mobile robots (AMRs) at hospitals where the stochastic travel times and service times of AMRs are affe...