Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Memristive synapses from biomaterials are promising for building flexible and implantable artificial neuromorphic systems due to their remarkable mechanical and biological properties. However, these biological devices have relatively poor memristive switching characteristics, and thus fail to meet the requirement of neuromorphic networks for high learning accuracy. Here, memristive synapses based ...
Matching resources to demand is a daily challenge for hospital leadership. In interdisciplinary collaboration, nurse leaders and data scientists collaborated to develop advanced machine learning to support early proactive decisions to improve ability to accommodate demand. When hundreds or even thousands of forecasts are made, it becomes important to let machines do the hard work of mathematical p...
Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can ben...
Estimating the category and quality of interpersonal relationships from ubiquitous phone sensor data matters for studying mental well-being and social...
OBJECTIVE: Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a cru...
Public water supply facilities are vulnerable to intentional intrusion. In particular, Water Distribution Network (WDN) has become one of the most imp...
The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. In order to enable consortium-wide r...
When humans perform cognitive tasks, it is necessary to hold information temporarily. This is done by a brain function called working memory (WM). Sin...
Proprioceptive deficits are common among stroke survivors and are associated with slower motor recovery, poorer upper limb motor function, and decreas...
Artificial intelligence (AI) has existed for decades and continues to evolve as technology advances. In health care, AI can be used to simplify the ch...
Hardware artificial neural network (ANN) systems with high density synapse array devices can perform massive parallel computing for pattern recognitio...
The race to make the dream of artificial intelligence a reality comes parallel with the increasing struggle of health care systems to cope with inform...
The aim of this systematic review and meta-analysis was to evaluate the effectiveness of robot-assisted gait training (RAGT) on motor impairments in p...
Spectral efficient frequency division multiplexing (SEFDM) can improve the spectral efficiency for next-generation optical and wireless communications...
The UNAIDS 90-90-90 target has prioritized achieving high rates of viral suppression. We identified factors associated with viral suppression among HI...
This study is aimed at assessing of a robot intervention in a task-based upper-arm rehabilitation procedure. Robotic devices have significantly been u...
Clinical investigators have asserted patients should be active participants in the therapy process in stroke rehabilitation. While robotics introduces...
Motor impairment is the most common symptom in multiple sclerosis (MS). Thus, a variety of new rehabilitative strategies, including robotic gait train...
OBJECTIVE: Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to pr...
This study aimed to identify the effects of rhythmic arm swing during robot-assisted walking training on balance, gait, motor function, and activities...