Latest AI and machine learning research in health policy for healthcare professionals.
Despite 30 years as a public health emergency, tuberculosis (TB) remains one of the world's deadliest diseases. Most deaths are among persons with TB who are not reached with diagnosis and treatment. Thus, timely screening and accurate detection of TB, particularly using sensitive tools such as chest radiography, is crucial for reducing the global burden of this disease. However, lack of qualified...
Real-world robot applications usually require navigating agents to face multiple destinations. Besides, the real-world crowded environments usually contain dynamic and static crowds that implicitly interact with each other during navigation. To address this challenging task, a novel modular hierarchical reinforcement learning (MHRL) method is developed in this paper. MHRL is composed of three modu...
The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent yea...
The use of medical data for machine learning, including unsupervised methods such as clustering, is often restricted by privacy regulations such as th...
The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand sim...
Ensuring the safety and reliability of drinking water supply requires accurate prediction of water quality in water distribution networks (WDNs). Howe...
When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection...
The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness ap...
Although great progress has been made in the diagnostic and treatment options for dyslipidemias, unawareness, underdiagnosis and undertreatment of the...
The ability of some artificial intelligence (AI) systems to autonomously evolve and the sometimes very limited possibilities to comprehend their decis...
INTRODUCTION: Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global ...
There is increasing use of machine learning for the health financing functions (revenue raising, pooling and purchasing), yet evidence lacks for its e...
Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (...
The development of virtual care options, including virtual hospital platforms, is rapidly changing the healthcare, mostly in the pandemic period, due ...
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recogn...
PURPOSE: Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This pap...
Artificial intelligence (AI) will drastically change the healthcare system. Radiology is one speciality that is most affected as AI algorithms are inc...
Artificial intelligence (AI) is a technology that is evolving rapidly and is changing the world and medicine as we know it. After reviewing the PROSPE...
The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in ...
Robots can traverse sparse obstacles by sensing environmental geometry and avoiding contact with obstacles. However, for search and rescue in rubble, ...