BACKGROUND: The widespread incidence and prevalence of Alzheimer's disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment.
Machine learning-based early warning systems (EWSs) can detect clinical deterioration more accurately than point-score tools. In patients with sepsis, however, the timing and scope of sepsis interventions relative to an advanced EWS alert are not wel...
OBJECTIVE: To explore the impact of rehabilitation robot training (RRT) on upper limb motor function and daily activity ability in patients with stroke.
Alzheimer disease and associated disorders
Jan 1, 2020
BACKGROUND: Incidence of dementia increases exponentially with age; little is known about its risk factors in the ninth and 10th decades of life. We identified predictors of dementia with onset after age 85 years in a longitudinal population-based co...
Journal of X-ray science and technology
Jan 1, 2020
OBJECTIVE: Diagnosis of tuberculosis (TB) in multi-slice spiral computed tomography (CT) images is a difficult task in many TB prevalent locations in which experienced radiologists are lacking. To address this difficulty, we develop an automated dete...
American journal of Alzheimer's disease and other dementias
Jan 1, 2020
BACKGROUND: The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically.
BACKGROUND: Because Alzheimer's Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations.
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