Latest AI and machine learning research in health policy for healthcare professionals.
There is a well-established tradition within the statistics literature that explores different techniques for reducing the dimensionality of large feature spaces. The problem is central to machine learning and it has been largely explored under the unsupervised learning paradigm. We introduce a supervised clustering methodology that capitalizes on a Metropolis Hastings algorithm to optimize the pa...
Accurate recurrence risk assessment in hormone receptor positive, HER2/neu negative breast cancer is critical to plan precise therapy. CanAssist Breast (CAB) assesses recurrence risk based on tumor biology using artificial intelligence-based approach. We report CAB risk assessment correlating with disease outcomes in multiple clinically high- and low-risk subgroups. In this retrospective cohort of...
Using "human-in-the-loop" (HIL) optimization can obtain suitable exoskeleton assistance patterns to improve walking economy. However, there are differ...
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be ...
Medication therapy management (MTM) and comprehensive medication management (CMM) have been practiced by clinical pharmacists as a predominantly manu...
Hit-and-run crashes not only degrade the morality, but also result in delays of medical services provided to victims. However, class imbalance problem...
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have ...
Rapid developments of robotics and virtual reality technology are raising the requirements of more advanced human-machine interfaces for achieving eff...
It is well known that information technology (IT) can play a pivotal role in enhancing healthcare quality and patient safety. The use of computational...
Many countries face major challenges to ensure that their health and social care systems are ready for the growing numbers of older people (OP). As a ...
Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restr...
The tiny encryption algorithm (TEA) is widely used when performing dissipative particle dynamics (DPD) calculations in parallel, usually on distribute...
Although progress is being made in affective computing, issues remain in enabling the effective expression of compassionate communication by healthcar...
BACKGROUND: Innovations in artificial intelligence (AI) have proven to be effective contributors to high-quality health care. We examined the benefici...
Rising rates of NCDs threaten fragile healthcare systems in low- and middle-income countries. Fortunately, new digital technology provides tools to mo...
Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy co...
The Bayesian method is capable of capturing real-world uncertainties/incompleteness and properly addressing the overfitting issue faced by deep neural...
Evidence-based STI (science, technology, and innovation) policy making requires accurate indicators of innovation in order to promote economic growth....
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperat...
In the healthcare domain, trust, confidence, and functional understanding are critical for decision support systems, therefore, presenting challenges ...