BACKGROUND AND OBJECTIVES: Clinical staff providing addiction treatment predict patient outcome poorly. Prognoses based on linear statistics are rarely replicated. Addiction is a complex non-linear behavior. Incorporating non-linear models, Machine L...
BACKGROUND: Robot-assisted gait training (RAGT) was developed to restore gait function by promoting neuroplasticity through repetitive locomotor training and has been utilized in gait training. However, contradictory outcomes of RAGT have been report...
Clinical trials are widely considered the gold standard for treatment evaluation, and they can be highly expensive in terms of time and money. The efficiency of clinical trials can be improved by incorporating information from baseline covariates tha...
The International journal of neuroscience
Nov 22, 2018
PURPOSE: The primary focus of this review was to find out the effectiveness of robotics in improving upper extremity functions among people with neurological problems in the arena of physical rehabilitation.
BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.
The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and...
BACKGROUND: The large amount of clinical signals in intensive care units can easily overwhelm health-care personnel and can lead to treatment delays, suboptimal care, or clinical errors. The aim of this study was to apply deep machine learning method...
Disability and rehabilitation. Assistive technology
Sep 21, 2018
To assess the effects of lower limb robotic exoskeletons on outcomes in the rehabilitation of people with acquired brain injury. A systematic review of seven electronic databases was conducted. The primary outcome of interest was neuromuscular funct...
IMPORTANCE: Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contri...
OBJECTIVE: We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lob...
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