AIMC Topic: Young Adult
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A Multi-Mode Rehabilitation Robot With Magnetorheological Actuators Based on Human Motion Intention Estimation.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 22, 2019
Lower extremity paralysis has become common in recent years, and robots have been developed to help patients recover from it. This paper presents such a robotic system that allows for two working modes, the robot-active mode and human-active mode. Th...
Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Aug 22, 2019
Considering the unsatisfactory classification accuracy of autism due to unsuitable features selected in current studies, a functional connectivity (FC)-based algorithm for classifying autism and control using support vector machine-recursive feature ...
Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos).
Gastrointestinal endoscopy
Aug 21, 2019
BACKGROUND AND AIMS: We developed a system for computer-assisted diagnosis (CAD) for real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinomas (ESCCs) to assist the diagnosis of esophageal cancer.
Machine Learning Diagnosis of Peritonsillar Abscess.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Aug 20, 2019
Peritonsillar abscess (PTA) is a difficult diagnosis to make clinically, with clinical examination of even otolaryngologists showing poor sensitivity and specificity. Machine learning is a form of artificial intelligence that "learns" from data to ma...
Cognitive prostheses for goal achievement.
Nature human behaviour
Aug 19, 2019
Procrastination takes a considerable toll on people's lives, the economy and society at large. Procrastination is often a consequence of people's propensity to prioritize their immediate experiences over the long-term consequences of their actions. T...
Documentation of Palliative and End-of-Life Care Process Measures Among Young Adults Who Died of Cancer: A Natural Language Processing Approach.
Journal of adolescent and young adult oncology
Aug 14, 2019
Few studies have investigated palliative and end-of-life care processes among young adults (YAs), aged 18-34 years, who died of cancer. This retrospective study used a natural language processing algorithm to identify documentation and timing of four...
A controller for walking derived from how humans recover from perturbations.
Journal of the Royal Society, Interface
Aug 14, 2019
Humans can walk without falling despite some external perturbations, but the control mechanisms by which this stability is achieved have not been fully characterized. While numerous walking simulations and robots have been constructed, no full-state ...
Dynamic network modeling and dimensionality reduction for human ECoG activity.
Journal of neural engineering
Aug 14, 2019
OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far...
Physiological indices of challenge and threat: A data-driven investigation of autonomic nervous system reactivity during an active coping stressor task.
Psychophysiology
Aug 13, 2019
We utilized a data-driven, unsupervised machine learning approach to examine patterns of peripheral physiological responses during a motivated performance context across two large, independent data sets, each with multiple peripheral physiological me...
Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.
EBioMedicine
Aug 13, 2019
BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.
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